{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第3章 数据的描述统计量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 本章需要用到的库\n",
    "import numpy as np # 导入numpy库\n",
    "import pandas as pd # 导入pandas库\n",
    "from scipy import stats # 导入scipy库stats模块\n",
    "import matplotlib.pyplot as plt # 导入matplotlib库\n",
    "import seaborn as sns # 导入seaborn库\n",
    "\n",
    "# 设置初始化\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 描述水平的统计量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1 平均数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   分数\n",
       "0  85\n",
       "1  55\n",
       "2  91\n",
       "3  66\n",
       "4  79"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example3_1 = pd.read_csv('./pydata/chap03/example3_1.csv', encoding='gbk') # 读取数据\n",
    "example3_1.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "分数    80.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.mean(example3_1) # 计算均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "分数    80.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example3_1.mean() # 计算均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(example3_1) # 计算均值 numpy库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分组</th>\n",
       "      <th>组中值</th>\n",
       "      <th>人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60以下</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60~70</td>\n",
       "      <td>65</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70~80</td>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80~90</td>\n",
       "      <td>85</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90~100</td>\n",
       "      <td>95</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       分组  组中值  人数\n",
       "0    60以下   55   3\n",
       "1   60~70   65   4\n",
       "2   70~80   75   4\n",
       "3   80~90   85  10\n",
       "4  90~100   95   9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example3_2 = pd.read_csv('./pydata/chap03/example3_2.csv', encoding='gbk') # 读取数据\n",
    "example3_2 # 显示数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = example3_2['组中值']\n",
    "f = example3_2['人数']\n",
    "np.average(m, weights=f) # 计算加权平均数"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 分位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "分数    85.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example3_1.median() # 计算中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        分数\n",
       "0.25  70.5\n",
       "0.75  90.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.quantile(example3_1, q=[0.25, 0.75], interpolation='linear') # 计算四分位数 线性插值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>60.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>92.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        分数\n",
       "0.10  60.4\n",
       "0.25  70.5\n",
       "0.50  85.0\n",
       "0.75  90.0\n",
       "0.90  92.3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.quantile(example3_1, q=[0.1, 0.25, 0.5, 0.75, 0.9], interpolation='linear') # 计算百分位数 线性插值"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.3 众数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   分数\n",
       "0  86"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example3_1.mode() # 计算众数"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 描述差异的统计量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1 极差和四分位差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "极差: 47\n"
     ]
    }
   ],
   "source": [
    "R = example3_1['分数'].max() - example3_1['分数'].min() # 计算极差\n",
    "print('极差:', R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR: 19.5\n"
     ]
    }
   ],
   "source": [
    "IQR = example3_1['分数'].quantile(0.75) - example3_1['分数'].quantile(0.25) # 计算四分位差\n",
    "print('IQR:', IQR)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.2 方差和标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "方差: 174.6206896551724\n"
     ]
    }
   ],
   "source": [
    "var = example3_1['分数'].var(ddof=1) # 计算方差 样本方差 ddof=1\n",
    "print('方差:', var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标准差: 13.214412194841374\n"
     ]
    }
   ],
   "source": [
    "sd = example3_1['分数'].std(ddof=1) # 计算标准差 样本标准差 ddof=1\n",
    "print('标准差:', sd)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.3 变异系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>北京</th>\n",
       "      <th>上海</th>\n",
       "      <th>郑州</th>\n",
       "      <th>武汉</th>\n",
       "      <th>西安</th>\n",
       "      <th>沈阳</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020/1/1</th>\n",
       "      <td>62</td>\n",
       "      <td>54</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>117</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/1/2</th>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>124</td>\n",
       "      <td>105</td>\n",
       "      <td>149</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/1/3</th>\n",
       "      <td>82</td>\n",
       "      <td>72</td>\n",
       "      <td>192</td>\n",
       "      <td>130</td>\n",
       "      <td>209</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/1/4</th>\n",
       "      <td>74</td>\n",
       "      <td>88</td>\n",
       "      <td>249</td>\n",
       "      <td>142</td>\n",
       "      <td>252</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/1/5</th>\n",
       "      <td>83</td>\n",
       "      <td>63</td>\n",
       "      <td>276</td>\n",
       "      <td>80</td>\n",
       "      <td>152</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          北京  上海   郑州   武汉   西安   沈阳\n",
       "日期                                  \n",
       "2020/1/1  62  54   73   73  117  109\n",
       "2020/1/2  80  82  124  105  149  214\n",
       "2020/1/3  82  72  192  130  209  204\n",
       "2020/1/4  74  88  249  142  252  207\n",
       "2020/1/5  83  63  276   80  152  198"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example2_3 = pd.read_csv('./pydata/chap02/example2_3.csv', encoding='gbk') # 读取数据\n",
    "example2_3 = example2_3.set_index('日期') # 将日期设置为索引\n",
    "example2_3.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均数</th>\n",
       "      <th>标准差</th>\n",
       "      <th>变异系数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>78.4454</td>\n",
       "      <td>41.6278</td>\n",
       "      <td>0.5307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>67.9399</td>\n",
       "      <td>28.9772</td>\n",
       "      <td>0.4265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>郑州</th>\n",
       "      <td>98.9071</td>\n",
       "      <td>42.7207</td>\n",
       "      <td>0.4319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>武汉</th>\n",
       "      <td>72.1530</td>\n",
       "      <td>30.3549</td>\n",
       "      <td>0.4207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西安</th>\n",
       "      <td>91.7896</td>\n",
       "      <td>45.7087</td>\n",
       "      <td>0.4980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>沈阳</th>\n",
       "      <td>78.5710</td>\n",
       "      <td>40.6698</td>\n",
       "      <td>0.5176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        平均数      标准差    变异系数\n",
       "北京  78.4454  41.6278  0.5307\n",
       "上海  67.9399  28.9772  0.4265\n",
       "郑州  98.9071  42.7207  0.4319\n",
       "武汉  72.1530  30.3549  0.4207\n",
       "西安  91.7896  45.7087  0.4980\n",
       "沈阳  78.5710  40.6698  0.5176"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_mean = example2_3.mean() # 计算均值\n",
    "s_sd = example2_3.std() # 计算标准差\n",
    "s_cv = s_sd / s_mean # 计算变异系数\n",
    "df = pd.DataFrame({'平均数': s_mean, '标准差': s_sd, '变异系数': s_cv}) # 创建数据框\n",
    "np.round(df, 4) # 保留4位小数"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.4 标准分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   分数\n",
       "0  85\n",
       "1  55\n",
       "2  91\n",
       "3  66\n",
       "4  79"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example3_1 = pd.read_csv('./pydata/chap03/example3_1.csv', encoding='gbk') # 读取数据\n",
    "example3_1.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.3784\n",
       "1    -1.8919\n",
       "2     0.8324\n",
       "3    -1.0594\n",
       "4    -0.0757\n",
       "5     1.2865\n",
       "6     0.9081\n",
       "7    -1.8919\n",
       "8     0.5297\n",
       "9     0.7567\n",
       "10    0.2270\n",
       "11   -0.7567\n",
       "12    1.2108\n",
       "13   -0.6054\n",
       "14    0.7567\n",
       "15   -1.4378\n",
       "16    0.4540\n",
       "17    0.4540\n",
       "18    0.9081\n",
       "19    0.3784\n",
       "20   -0.9838\n",
       "21    0.0757\n",
       "22    0.6811\n",
       "23   -2.2702\n",
       "24    1.1351\n",
       "25    0.4540\n",
       "26   -0.3784\n",
       "27    0.8324\n",
       "28    0.1513\n",
       "29   -1.0594\n",
       "Name: 分数, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = stats.zscore(example3_1['分数'], ddof=1) # 计算标准分数\n",
    "np.round(z, 4) # 保留4位小数"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 描述分布形状的统计量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.1 偏度系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   分数\n",
       "0  85\n",
       "1  55\n",
       "2  91\n",
       "3  66\n",
       "4  79"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example3_1 = pd.read_csv('./pydata/chap03/example3_1.csv', encoding='gbk') # 读取数据\n",
    "example3_1.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "偏度系数: -0.8314\n"
     ]
    }
   ],
   "source": [
    "skew = example3_1['分数'].skew() # 计算偏度系数\n",
    "print('偏度系数:', round(skew, 4))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.2 峰度系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "峰度系数: -0.3515\n"
     ]
    }
   ],
   "source": [
    "kurt = example3_1['分数'].kurt() # 计算峰度系数\n",
    "print('峰度系数:', round(kurt, 4))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 一个综合描述的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>家庭所在地</th>\n",
       "      <th>月生活费支出</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女</td>\n",
       "      <td>中小城市</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男</td>\n",
       "      <td>大型城市</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>男</td>\n",
       "      <td>大型城市</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>女</td>\n",
       "      <td>中小城市</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>女</td>\n",
       "      <td>中小城市</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  性别 家庭所在地  月生活费支出\n",
       "0  女  中小城市    1500\n",
       "1  男  大型城市    2000\n",
       "2  男  大型城市    1800\n",
       "3  女  中小城市    1600\n",
       "4  女  中小城市    2000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example3_12 = pd.read_csv('./pydata/chap03/example3_12.csv', encoding='gbk') # 读取数据\n",
    "example3_12.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 月生活费支出的直方图和箱型图\n",
    "x = example3_12['月生活费支出']\n",
    "\n",
    "fig = plt.figure(figsize=(6, 5)) # 设置画布大小\n",
    "spec = fig.add_gridspec(nrows=2, ncols=1, height_ratios=[1, 3]) # 设置子图网格\n",
    "\n",
    "ax1 = fig.add_subplot(spec[0, :]) # 创建子图1\n",
    "x.plot(kind='box', ax=ax1, vert=False) # 绘制箱线图\n",
    "ax1.spines['right'].set_color('none') # 去掉右边框\n",
    "ax1.spines['top'].set_color('none') # 去掉上边框\n",
    "ax1.spines['left'].set_color('none') # 去掉左边框\n",
    "ax1.spines['bottom'].set_color('none') # 去掉下边框\n",
    "ax1.set_xticks([]) # 去掉x轴刻度\n",
    "\n",
    "ax2 = fig.add_subplot(spec[1, :]) # 创建子图2\n",
    "x.plot(kind='hist', bins=10, ax=ax2, density=True, legend=False) # 绘制直方图\n",
    "x.plot(kind='density', ax=ax2, linewidth=3) # 绘制核密度图\n",
    "ax2.set_xlabel('月生活费支出') # 设置x轴标签\n",
    "ax2.set_ylabel('频率') # 设置y轴标签\n",
    "ax2.set_xlim(x.min(), x.max()) # 设置x轴范围\n",
    "ax2.spines['right'].set_color('none') # 去掉右边框\n",
    "ax2.spines['top'].set_color('none') # 去掉上边框\n",
    "\n",
    "plt.tight_layout() # 设置子图间距\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按性别和家庭所在地分组绘制核密度图\n",
    "plt.figure(figsize=(12, 4)) # 设置画布大小\n",
    "\n",
    "plt.subplot(1, 2, 1) # 创建子图1\n",
    "sns.kdeplot(x='月生活费支出', hue='性别', data=example3_12, fill=True) # 绘制核密度图\n",
    "plt.title('(a) 按性别分组') # 设置标题\n",
    "\n",
    "plt.subplot(1, 2, 2) # 创建子图2\n",
    "sns.kdeplot(x='月生活费支出', hue='家庭所在地', data=example3_12, fill=True) # 绘制核密度图\n",
    "plt.title('(b) 按家庭所在地分组') # 设置标题\n",
    "\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按性别和家庭所在地分组绘制箱型图\n",
    "plt.figure(figsize=(9, 6)) # 设置画布大小\n",
    "sns.boxplot(x='性别', y='月生活费支出', hue='家庭所在地', data=example3_12) # 绘制箱型图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按性别和家庭所在地分组绘制点图\n",
    "plt.figure(figsize=(9, 4)) # 设置画布大小\n",
    "\n",
    "plt.subplot(121) # 创建子图1\n",
    "sns.stripplot(\n",
    "    x='性别', y='月生活费支出', data=example3_12, \n",
    "    jitter=True, size=5\n",
    ") # 绘制点图\n",
    "plt.title('(a) 按性别分组') # 设置标题\n",
    "\n",
    "plt.subplot(122) # 创建子图2\n",
    "sns.stripplot(\n",
    "    x='家庭所在地', y='月生活费支出', data=example3_12,\n",
    "    jitter=True, size=5\n",
    ") # 绘制点图\n",
    "plt.title('(b) 按家庭所在地分组') # 设置标题\n",
    "\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">月生活费支出</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>家庭所在地</th>\n",
       "      <th>中小城市</th>\n",
       "      <th>乡镇地区</th>\n",
       "      <th>大型城市</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>女</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>男</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>合计</th>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      月生活费支出              \n",
       "家庭所在地   中小城市 乡镇地区 大型城市  合计\n",
       "性别                        \n",
       "女         18    6   11  35\n",
       "男          6    4   15  25\n",
       "合计        24   10   26  60"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab = pd.pivot_table(\n",
    "    example3_12, index=['性别'], columns=['家庭所在地'],\n",
    "    margins=True, margins_name='合计', aggfunc=len\n",
    ") # 创建透视表\n",
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "全部学生支出的概括性描述\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>月生活费支出</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>60.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1812.3333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>320.9962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1550.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1850.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2800.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          月生活费支出\n",
       "count    60.0000\n",
       "mean   1812.3333\n",
       "std     320.9962\n",
       "min    1100.0000\n",
       "25%    1550.0000\n",
       "50%    1850.0000\n",
       "75%    2000.0000\n",
       "max    2800.0000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc = example3_12.describe(include=None) # 计算描述统计量 不包括类别变量\n",
    "print('全部学生支出的概括性描述')\n",
    "round(desc, 4) # 保留4位小数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>月生活费支出</th>\n",
       "      <th>月生活费支出</th>\n",
       "      <th>月生活费支出</th>\n",
       "      <th>月生活费支出</th>\n",
       "      <th>月生活费支出</th>\n",
       "      <th>月生活费支出</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th>家庭所在地</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">女</th>\n",
       "      <th>中小城市</th>\n",
       "      <td>33140</td>\n",
       "      <td>1400</td>\n",
       "      <td>2360</td>\n",
       "      <td>1825</td>\n",
       "      <td>1841.111111</td>\n",
       "      <td>308.942959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乡镇地区</th>\n",
       "      <td>10720</td>\n",
       "      <td>1300</td>\n",
       "      <td>2000</td>\n",
       "      <td>1860</td>\n",
       "      <td>1786.666667</td>\n",
       "      <td>247.521043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大型城市</th>\n",
       "      <td>22350</td>\n",
       "      <td>1500</td>\n",
       "      <td>2800</td>\n",
       "      <td>2000</td>\n",
       "      <td>2031.818182</td>\n",
       "      <td>383.583581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">男</th>\n",
       "      <th>中小城市</th>\n",
       "      <td>9960</td>\n",
       "      <td>1300</td>\n",
       "      <td>2000</td>\n",
       "      <td>1725</td>\n",
       "      <td>1660.000000</td>\n",
       "      <td>290.172363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乡镇地区</th>\n",
       "      <td>6850</td>\n",
       "      <td>1500</td>\n",
       "      <td>1950</td>\n",
       "      <td>1700</td>\n",
       "      <td>1712.500000</td>\n",
       "      <td>246.221445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大型城市</th>\n",
       "      <td>25720</td>\n",
       "      <td>1100</td>\n",
       "      <td>2000</td>\n",
       "      <td>1780</td>\n",
       "      <td>1714.666667</td>\n",
       "      <td>293.400425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>合计</th>\n",
       "      <th></th>\n",
       "      <td>108740</td>\n",
       "      <td>1100</td>\n",
       "      <td>2800</td>\n",
       "      <td>1850</td>\n",
       "      <td>1812.333333</td>\n",
       "      <td>318.309947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sum    min    max median         mean         std\n",
       "          月生活费支出 月生活费支出 月生活费支出 月生活费支出       月生活费支出      月生活费支出\n",
       "性别 家庭所在地                                                      \n",
       "女  中小城市    33140   1400   2360   1825  1841.111111  308.942959\n",
       "   乡镇地区    10720   1300   2000   1860  1786.666667  247.521043\n",
       "   大型城市    22350   1500   2800   2000  2031.818182  383.583581\n",
       "男  中小城市     9960   1300   2000   1725  1660.000000  290.172363\n",
       "   乡镇地区     6850   1500   1950   1700  1712.500000  246.221445\n",
       "   大型城市    25720   1100   2000   1780  1714.666667  293.400425\n",
       "合计        108740   1100   2800   1850  1812.333333  318.309947"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab = pd.pivot_table(\n",
    "    example3_12, index=['性别', '家庭所在地'], values=['月生活费支出'],\n",
    "    margins=True, margins_name='合计', aggfunc=[sum, min, max, np.median, np.mean, np.std]\n",
    ") # 创建透视表\n",
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按性别分组\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>平均数</th>\n",
       "      <th>中位数</th>\n",
       "      <th>标准差</th>\n",
       "      <th>全距</th>\n",
       "      <th>变异系数</th>\n",
       "      <th>偏度系数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>女</th>\n",
       "      <td>35</td>\n",
       "      <td>1891.714</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>331.1521</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.175054</td>\n",
       "      <td>0.502824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>男</th>\n",
       "      <td>25</td>\n",
       "      <td>1701.200</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>275.4893</td>\n",
       "      <td>900</td>\n",
       "      <td>0.161938</td>\n",
       "      <td>-0.548589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     n       平均数     中位数       标准差    全距      变异系数      偏度系数\n",
       "性别                                                          \n",
       "女   35  1891.714  1900.0  331.1521  1500  0.175054  0.502824\n",
       "男   25  1701.200  1780.0  275.4893   900  0.161938 -0.548589"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_summary(df, col=['性别']):\n",
    "    df_res = pd.DataFrame()\n",
    "    df_res['n'] = df.groupby(col)['月生活费支出'].count() # 计算频数\n",
    "    df_res['平均数'] = df.groupby(col)['月生活费支出'].mean().round(3) # 计算均值\n",
    "    df_res['中位数'] = df.groupby(col)['月生活费支出'].median() # 计算中位数\n",
    "    df_res['标准差'] = df.groupby(col)['月生活费支出'].std().round(4) # 计算标准差\n",
    "    df_res['全距'] = df.groupby(col)['月生活费支出'].apply(lambda x: x.max() - x.min()) # 计算全距\n",
    "    df_res['变异系数'] = df.groupby(col)['月生活费支出'].apply(lambda x: x.std() / x.mean()) # 计算变异系数\n",
    "    df_res['偏度系数'] = df.groupby(col)['月生活费支出'].skew() # 计算偏度系数\n",
    "    return df_res\n",
    "\n",
    "df1 = my_summary(example3_12, ['性别']) # 计算描述统计量\n",
    "print('按性别分组')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按家庭所在地分组\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>平均数</th>\n",
       "      <th>中位数</th>\n",
       "      <th>标准差</th>\n",
       "      <th>全距</th>\n",
       "      <th>变异系数</th>\n",
       "      <th>偏度系数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>家庭所在地</th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中小城市</th>\n",
       "      <td>24</td>\n",
       "      <td>1795.833</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>308.6565</td>\n",
       "      <td>1060</td>\n",
       "      <td>0.171874</td>\n",
       "      <td>0.269364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乡镇地区</th>\n",
       "      <td>10</td>\n",
       "      <td>1757.000</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>236.0344</td>\n",
       "      <td>700</td>\n",
       "      <td>0.134339</td>\n",
       "      <td>-1.052810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大型城市</th>\n",
       "      <td>26</td>\n",
       "      <td>1848.846</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>364.1354</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.196953</td>\n",
       "      <td>0.321079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        n       平均数     中位数       标准差    全距      变异系数      偏度系数\n",
       "家庭所在地                                                          \n",
       "中小城市   24  1795.833  1800.0  308.6565  1060  0.171874  0.269364\n",
       "乡镇地区   10  1757.000  1860.0  236.0344   700  0.134339 -1.052810\n",
       "大型城市   26  1848.846  1850.0  364.1354  1700  0.196953  0.321079"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = my_summary(example3_12, ['家庭所在地']) # 计算描述统计量\n",
    "print('按家庭所在地分组')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "同时按性别和家庭所在地分组\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>平均数</th>\n",
       "      <th>中位数</th>\n",
       "      <th>标准差</th>\n",
       "      <th>全距</th>\n",
       "      <th>变异系数</th>\n",
       "      <th>偏度系数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th>家庭所在地</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">女</th>\n",
       "      <th>中小城市</th>\n",
       "      <td>18</td>\n",
       "      <td>1841.111</td>\n",
       "      <td>1825.0</td>\n",
       "      <td>308.9430</td>\n",
       "      <td>960</td>\n",
       "      <td>0.167802</td>\n",
       "      <td>0.345656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乡镇地区</th>\n",
       "      <td>6</td>\n",
       "      <td>1786.667</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>247.5210</td>\n",
       "      <td>700</td>\n",
       "      <td>0.138538</td>\n",
       "      <td>-2.042846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大型城市</th>\n",
       "      <td>11</td>\n",
       "      <td>2031.818</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>383.5836</td>\n",
       "      <td>1300</td>\n",
       "      <td>0.188788</td>\n",
       "      <td>0.515560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">男</th>\n",
       "      <th>中小城市</th>\n",
       "      <td>6</td>\n",
       "      <td>1660.000</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>290.1724</td>\n",
       "      <td>700</td>\n",
       "      <td>0.174803</td>\n",
       "      <td>-0.276271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乡镇地区</th>\n",
       "      <td>4</td>\n",
       "      <td>1712.500</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>246.2214</td>\n",
       "      <td>450</td>\n",
       "      <td>0.143779</td>\n",
       "      <td>0.035589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大型城市</th>\n",
       "      <td>15</td>\n",
       "      <td>1714.667</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>293.4004</td>\n",
       "      <td>900</td>\n",
       "      <td>0.171112</td>\n",
       "      <td>-0.763562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           n       平均数     中位数       标准差    全距      变异系数      偏度系数\n",
       "性别 家庭所在地                                                          \n",
       "女  中小城市   18  1841.111  1825.0  308.9430   960  0.167802  0.345656\n",
       "   乡镇地区    6  1786.667  1860.0  247.5210   700  0.138538 -2.042846\n",
       "   大型城市   11  2031.818  2000.0  383.5836  1300  0.188788  0.515560\n",
       "男  中小城市    6  1660.000  1725.0  290.1724   700  0.174803 -0.276271\n",
       "   乡镇地区    4  1712.500  1700.0  246.2214   450  0.143779  0.035589\n",
       "   大型城市   15  1714.667  1780.0  293.4004   900  0.171112 -0.763562"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = my_summary(example3_12, ['性别', '家庭所在地']) # 计算描述统计量\n",
    "print('同时按性别和家庭所在地分组')\n",
    "df3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 习题"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>网购金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   网购金额\n",
       "0  1004\n",
       "1   921\n",
       "2   928\n",
       "3  1148\n",
       "4  1416"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap03/exercise3_1.csv', encoding='gbk') # 读取数据\n",
    "df.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均数</th>\n",
       "      <th>标准差</th>\n",
       "      <th>变异系数</th>\n",
       "      <th>极差</th>\n",
       "      <th>四分位差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>网购金额</th>\n",
       "      <td>1032.64</td>\n",
       "      <td>385.3728</td>\n",
       "      <td>0.3732</td>\n",
       "      <td>1981</td>\n",
       "      <td>402.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          平均数       标准差    变异系数    极差    四分位差\n",
       "网购金额  1032.64  385.3728  0.3732  1981  402.75"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 计算平均数、标准差、极差和四分位差。\n",
    "s_mean = df.mean() # 计算均值\n",
    "s_sd = df.std() # 计算标准差\n",
    "s_cv = s_sd / s_mean # 计算变异系数\n",
    "s_R = df.max() - df.min() # 计算极差\n",
    "s_IQR = df.quantile(0.75) - df.quantile(0.25) # 计算四分位差\n",
    "df1 = pd.DataFrame({'平均数': s_mean, '标准差': s_sd, '变异系数': s_cv, '极差': s_R, '四分位差': s_IQR}) # 创建数据框\n",
    "np.round(df1, 4) # 保留4位小数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>网购金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>565.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>838.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>985.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>1241.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>1482.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         网购金额\n",
       "0.10   565.80\n",
       "0.25   838.50\n",
       "0.50   985.50\n",
       "0.75  1241.25\n",
       "0.90  1482.10"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 计算10%、25%、50%、75%和90%的分位数。\n",
    "df2 = pd.DataFrame.quantile(df, q=[0.1, 0.25, 0.5, 0.75, 0.9], interpolation='linear') # 计算百分位数 线性插值\n",
    "np.round(df2, 2) # 保留2位小数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>网购金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.0743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.2897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.2715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.9948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.1175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.1106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.2656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.4039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.9014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.3165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.7171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.6322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.2108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-1.3484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.3251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-1.3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.0380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.1963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.4013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.5051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.3312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.4966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.3027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.2534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-1.9297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.3461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.2715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.2430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.5562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.0762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.6912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>-0.7879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>-1.2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-0.9747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-1.7869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.9964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.5718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2.0327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.4343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>-0.1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>-1.0059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.1634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      网购金额\n",
       "0  -0.0743\n",
       "1  -0.2897\n",
       "2  -0.2715\n",
       "3   0.2993\n",
       "4   0.9948\n",
       "5  -1.1175\n",
       "6  -0.1106\n",
       "7   0.2656\n",
       "8  -0.4039\n",
       "9   0.9014\n",
       "10  1.3165\n",
       "11  0.7171\n",
       "12 -0.6322\n",
       "13  3.2108\n",
       "14 -1.3484\n",
       "15 -1.3251\n",
       "16 -1.3095\n",
       "17 -0.0380\n",
       "18 -0.1963\n",
       "19  1.0129\n",
       "20 -0.4013\n",
       "21 -0.5051\n",
       "22 -0.3312\n",
       "23 -0.4999\n",
       "24  0.4966\n",
       "25 -0.3027\n",
       "26  1.1920\n",
       "27 -0.2534\n",
       "28 -1.1849\n",
       "29 -1.9297\n",
       "30  0.3461\n",
       "31 -0.2715\n",
       "32 -0.2430\n",
       "33  0.2007\n",
       "34  0.5562\n",
       "35  0.0762\n",
       "36  0.6912\n",
       "37 -0.7879\n",
       "38 -1.2005\n",
       "39 -0.9747\n",
       "40 -1.7869\n",
       "41  0.1930\n",
       "42  1.9964\n",
       "43  0.5718\n",
       "44  2.0327\n",
       "45  0.4343\n",
       "46 -0.1340\n",
       "47 -1.0059\n",
       "48  0.2604\n",
       "49  1.1634"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (3) 计算标准分数，检测数据的离群点。\n",
    "df3 = stats.zscore(df, ddof=1) # 计算标准分数\n",
    "np.round(df3, 4) # 保留4位小数"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过标准分数可以找出离群值，一般而言3个标准差以外的值就可以认为是离群值。\n",
    "- 比如上面的第14个数据（索引为13），它的标准分数为3.2108，大于3，因此可以认为它是离群值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>偏度系数</th>\n",
       "      <th>峰度系数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>网购金额</th>\n",
       "      <td>0.6633</td>\n",
       "      <td>1.2203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        偏度系数    峰度系数\n",
       "网购金额  0.6633  1.2203"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (4) 计算偏度系数和峰度系数，分析网购金额的分布特征。\n",
    "skew = df.skew() # 计算偏度系数\n",
    "kurt = df.kurt() # 计算峰度系数\n",
    "df4 = pd.DataFrame({'偏度系数': skew, '峰度系数': kurt}) # 创建数据框\n",
    "np.round(df4, 4) # 保留4位小数"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "网购金额：\n",
    "- 偏度系数为0.6633，说明网购金额的分布形状是右偏；\n",
    "- 峰度系数为1.2203，说明网购金额的分布形状是尖峰。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>方法A</th>\n",
       "      <th>方法B</th>\n",
       "      <th>方法C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>164</td>\n",
       "      <td>129</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>167</td>\n",
       "      <td>130</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>168</td>\n",
       "      <td>129</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>165</td>\n",
       "      <td>130</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>170</td>\n",
       "      <td>131</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   方法A  方法B  方法C\n",
       "0  164  129  125\n",
       "1  167  130  126\n",
       "2  168  129  126\n",
       "3  165  130  127\n",
       "4  170  131  126"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap03/exercise3_2.csv', encoding='gbk') # 读取数据\n",
    "df.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>方法</th>\n",
       "      <th>产品数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>方法A</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>方法A</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>方法A</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>方法A</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>方法A</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    方法  产品数量\n",
       "0  方法A   164\n",
       "1  方法A   167\n",
       "2  方法A   168\n",
       "3  方法A   165\n",
       "4  方法A   170"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_melt = pd.melt(\n",
    "    df, value_vars=['方法A', '方法B', '方法C'], \n",
    "    var_name='方法', value_name='产品数量'\n",
    ") # 数据重构\n",
    "df_melt.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZZ57hrrvuwuVyAdE32bfeeitOp5Nf//rXdY5n9+7dLFmyhMmTJ7Ns2TLmzJnDnj17eOutt7juuuv48ssv6dq1a8LPU0QkGeqbryH6Brj6DavJZOKEE07gm2++qbXfzp0762y2HK/qgHFRURHbtm2r81ZaWlrnGKtZLBZeeOEFIpEI//3vf8nKyuKBBx7gyiuv5D//+Q89e/akuLhYXfJFpEXUN5euXbuWkSNH1tj2+OOPM2HChBrbmvPet6CggOeff54ZM2YAcMcdd9C7d298Ph9jxozBZrNx3nnnkZmZyXPPPccLL7zAmjVranwht3nzZtasWcOiRYs4/PDD+de//kUkEuG7775jzJgxmM1munTpwq5du7jzzjvJyclh2rRptVb/q6iooEuXLuzcuZOTTz6Zfv36MXz48Bq3oUOHNrh8rNFo5PXXX6ekpIQXX3yR0047jTvvvJNx48bx/vvvc9xxx8Ua/Uv7pf8bS7tw3HHHsXr1an7605/Gtj3++ONcc801/OpXv+Kqq65i+PDhjB49utaxq1atYvDgwU2+9vbt23niiSfo0qULXbp04Z133mHBggVxHWu321m1ahUZGRmUlZXh9/tjqXEGg4Fbb72VSZMmxaLxy5YtIxgM1rsE16mnnsoHH3wARAMm99xzDw899BDPP/883377Lb/97W+b/DxFRJKhrvkaaneUN5lMhEKhGtu+//57DAYDeXl5Tb5+dVCj+hvC+lSXqdTFaDTGAtGRSISbb745lhn4wAMP8NBDDwHw5JNPNnmcIiINqWsurSv74ttvv8Xj8XDcccfFtjXnve/vfvc7pkyZEst6PuSQQ3jjjTcA+OSTT7j00kux2+28/fbblJaWcs0113DNNddw2mmnxc7x+uuvc8stt3DRRRfx1FNPMWLECIxGIx6Ph+eee44LL7yQnJycBsexZcsWfD4fvXr1wufz8fHHH8f+n7F/AKK8vLxGhkhdHnzwQSD6+h111FGYTCZuuOEGrrrqqlhA5O23307wlZK2RJka0i5ccMEFLF68uMa2xx57jEsvvZQnnniCyy+/vN7aweeff75WqloipkyZwtChQ8nPz6dPnz48+uijvPrqqwAsXryYvLy82O2EE06odXzXrl3xeDz06tWLXr160bt3b3r37h2b/AcOHBjb1qtXL44//vhGx7Ry5UqOOOIIVq9ezZw5c/joo49qRbdFRFKhrvkaon2QqoXDYT755BOOOOKIGvssXryYCy64oFnlJ9nZ2YTDYSKRSK2b1+ut8e+KiopGzzd27FiuvvpqDjvsMD755JNa34iKiLSEuubSwYMHs3Llyhrbfvvb3zJ79uwa25rz3vdPf/oTd911V41tZWVl/O53v+OUU06hf//+rFq1CovFwrnnnovBYGDu3Lk19r/qqqvYuXMnQ4cO5a233ooFtb1eL1deeSUlJSWxfWfPns1vfvObWuNYt24dGRkZ9OnTh759+xIMBlmyZAk5OTkUFhZSVlbGvHnzOP/88xk6dGhcz+3KK6/kkksuwel0smLFCm688cZEXx5poxTUkHbhyiuv5LXXXmPdunWxbbm5ubz//vsUFBTwzDPPMHz4cCorK2s0GVq3bh3Lly9n8uTJTbruPffcw8qVK2t8G3fxxRfz7rvvAvCLX/yCoqKi2O3jjz+u8zzZ2dmx0pjqtbWrS1W2bdsW2+Z2u9m8eXOt4w/8NnPAgAHMmjWLb7/9lp/+9KecfPLJeL3eGvs0Z0lEEZGmqmu+Bvjiiy+YMmUKb775JpMmTWLr1q1MmzYt9viOHTt46KGH+L//+78mX/t///sfAwcOjM3ZN998M8uXLwei38IdddRRsf9H3HfffTW+WYTacy1EV0EZNGgQc+bM4aijjuKvf/1rjcc114pIS6hrLr3llltYsWIFU6dO5T//+Q+333477777LldeeWVsn+a+97344otrZVFkZmbi9/uZM2cOr732GhUVFYwaNYpdu3ZRWlrKzTffXOs8VquV7du312gIXZ2tvH+mnNForHMeffnll2vN0RdeeCFHHnkkkyZN4rXXXmP27Nn8+c9/rve5HDinX3311Zx++uk8/vjjDBgwgD/96U+1jtGc3j6p/ETahS5dujBnzhwmTpzIqlWryMrK4qGHHuK3v/0t559/Pnl5efz+97/nueeeY9WqVVxxxRWUl5czceJE/vSnP5Gdnd2k6/7617/muOOO48gjj4x98wfEOjDX1yh0/+ZOlZWVmM3mWh2ZqyfaUChUq9uz3+8nFAqRkZEBEAuI7P8/hupawq1bt/Lll19SWFgYeywYDMa1RKyISLLVNV8DTJo0ic2bNzN+/HgOP/xwli5dyqBBg4DonHf55Zdz9dVXc/jhhzf52g8//DDdu3dn4sSJALz22mv4/X7OOussBg0axLZt23jttdcYP348p59+Otdffz0rVqyI1ahXz5tbtmyJzbfdu3fnyiuvZNKkSSxbtizWPA+ifTs014pIS6hrLj377LN5+eWX+eMf/8jf/vY3jjjiCF566SXGjRsHkJT3vtXC4XDsfa3RaOQvf/kLkUiExYsXc+2113LwwQfzySef8PLLL3PttdeSnp5eK8PDYDDElXl34D5FRUX84x//iJWN7L/f7bffzplnnskrr7zCE088QZ8+feo9bzAYZOfOnbE5Oy0tjV/84hf84he/4L///S8HH3xw7LGtW7fGjpH2xxBpygLGIikydepUqqqqWLRoUaP7Tpw4kYyMjFqd9Zvq7LPP5vjjj+eOO+6IjcXv99fI4igsLOTQQw9l7969sSj3T37yE7766iusVmvc1wqFQhx//PG8//77sWs9+uijCY33vvvuizV5EhFpbYnM17feeiuff/45r7zySpNL6TZs2MBxxx3H7bffHvvW8OKLL6a8vJw333wTiJaSWCwWli1bBhBbweStt94CoKSkhO7duyd03YyMjLjKWEREmiJV733feOMNzjzzTILBIH6/nxdffJGHH36Yzz//nOnTpzN79uzYyiO/+c1vePrpp1m9enVspT+AmTNnMn/+/AavE4lEmDhxIk8//XRs25w5c3jggQfYsmUL//3vf1m1ahVbt27lww8/ZOfOnZx55pkEAgEKCgro27cvxx57LJmZmQwfPrxGs32n04nH40noee/Zs6dJDVYltZSpIe3KQw89RHFxcVz73n333Qm/OW2Iz+er0YXa5/PVG33ef7/PP/+8SR3y909/CwaDnHbaaaxYsSKuY6tXghERSZVE5uvp06djs9ma1Rvo0EMP5aGHHuLiiy+ObbvuuutqLLt61113kZubG7t/xx131FgOsHrefOedd2qtMFCXJ598kmuvvbbJYxYRaUyq3vsGAgEgmklnNBp56aWX6Nu3L3/729845phjao3x1FNPrRHQAJg7d26tfhsHuuuuu/jiiy9qbLv55psZOXIkDoeD7OxsvvzySwYNGsTPf/5zTj/9dJxOJxDNTHnttddYvXo133zzDVOmTKlxnmAwyN/+9rd6VxXc39tvv80ZZ5yh98/tlDI1RNqBPXv2EAgEmrUigIiINCwUCrF161by8vKw2+2pHo6ISJtR18orbd3mzZvJzc0lPT091UORFqaghoiIiIiIiIi0S1r9RERERERERETaJQU1RERERERERKRdUlBDRERERERERNqlTrX6STgcZseOHWRkZLS7Rjci0rFFIhEqKyvp2bMnRmPHjjdrLhaRtqozzcWg+VhE2q5E5uNOFdTYsWMHffr0SfUwRETqtXXrVnr37p3qYbQozcUi0tZ1hrkYNB+LSNsXz3zcqYIaGRkZQPSFyczMTPFoRER+VFFRQZ8+fWLzVEemuVhE2qrONBeD5mMRabsSmY87VVCjOq0uMzNTE7eItEmdIf1Xc7GItHWdYS4Gzcci0vbFMx93/GJBEREREREREemQFNQQERERERERkXZJQQ0RERERERERaZc6VU8NERERERERkaaKRCIEg0FCoVCqh9KumUwmzGZzUnoYKaghIiIiItJK1q5dy6RJk/jmm2/Iz8/n2Wef5aCDDmrwmIKCAq655hq2b9/Oueeey1//+lecTicrVqxg1KhRNfY95JBDKCwsbMFnINJ5+f1+du7cidvtTvVQOgSn00mPHj2wWq3NOo+CGiIiIiIirSAcDnPhhRfyy1/+kmXLljFt2jSmT5/Oc889V+8xZWVlXHLJJdx9992MGzeOSy+9lDlz5jBnzhyGDx9OaWlpbN9HHnmEZcuWtcZTEel0wuEwmzZtwmQy0bNnT6xWa6dZKSnZIpEIfr+fXbt2sWnTJo444giMxqZ3xkhpT42dO3dy2mmn8fnnnwNQWFiIwWCodau2du1aBg0ahMPhYMyYMZSUlKRo5CIiHYvmYxGRlrdy5Ur27t3L7bffTu/evZk1axZLly7F5XLVe8zSpUvp3bs3V111FX379uWGG27g2WefBcBsNpOdnR27rVixgnHjxrXW0xHpVPx+P+FwmJ49e5KVlYXD4cBut+vWhJvD4SArK4uePXsSDofx+/3N+t2kLKgxZcoUevbsybvvvhvbdvDBB1NaWhq7LVq0iLy8PODHyPY555zDxo0bcTgcTJ8+PVXDFxHpMDQfi4i0jjVr1nDiiSdiNkeTpQcNGkQoFGLt2rUNHnPKKafE7p900kls2bKFrVu31tivqqqKd999l7POOqvec/l8PioqKmrcRCQxzckokJqS9Vqm7DcyZ84cNm3aVGOb0WisEW1+9913Y9HmpkS2RUSkcZqPk6NgfRHjH17NgFvfYPzDqylYX5TqIYlIG1NUVETXrl1j941GIzk5ORQXF8d9TG5uLkCtY95++22ysrI4/vjj6z3X3LlzycrKit369OnT1KciItJmpKynRteuXWtM0HV5/fXXuf/++4GGI9v5+fktPt6miEQieL3epJ7P5/MBYLPZklrDZbfbVRMm0kl1hvm4pRWsL2LK3z+N3V+3rZyrnv2UxyYMZezReSkcmYi0NZFIpNb9xt6D7X9M9b8PPGb58uWMGzeuwXPNnDmTGTNmxO5XVFQosCHSjm3atAm73U6PHj1SPZSUarONQj///HOKi4s544wzgKZFtn0+XywIALR6ip3X62Xs2LGtes2mKigowOFwpHoYItIGNXc+TvVc3BoWvvNdrW2RCCxc8b2CGiIS06NHDzZs2BC7HwqFKCsri5X31XfMrl27Yvf37NkDUOuY119/nXvuuafB69tsNmw2W1OGLiJt0Pnnn8+4ceO4++6763x848aNLFq0iNtuuw2LxVLjsczMTD788EMGDBjQGkNtUW22IGj58uWcfPLJZGdnx7YlGtlWip2ISPM1dz7uDHPxt8VVdW7fWFzZyiMRkbYsPz+fjz/+mGAwCMBnn32G2Wxm8ODBDR7z3nvvxe5/8MEH9O3bl169esW2rVu3jh07drSbL9M6DfdeWPIb+PKfqR6JdFATJkwgHA7X+7jNZuONN95gxIgR7Nixo8ZjVqsVu93e0kNsFW02U2P58uWcffbZsftNiWynOsXObrdTUFCQtPN5vV7Gjx8PwKuvvprUP8KO8gctIsnX3Pk41XNxa+jfPZ1128prbT+ie0YKRiMibdXw4cPp1q0bs2bN4uqrr2b27NlccMEFOJ1OKioqcDgctb5NHT9+PNdeey2PPvooZ555JvPnz2fChAk19lm+fDknnXQSXbp0ac2nI4354FFY/zJsLID+48CaluoRSTv11VdfkZ+fj9PpxGQyAeByuXA4HBiNRpYsWRLbNxwO43a7Wb9+PQcffDCrV6/moYceIicnhwkTJvDee+9hNBopKytj1KhRmM1mvF4vixcvZsSIEal6is3SJoMapaWlfPDBBzzyyCOxbfn5+dx9990Eg0HMZnNcke1Up9gZDIYWK+moXgpHRKQlJWM+TvVc3BqmjurHVc9+yv4JLAYDTBt5eOoGJSJtTvWHj9/85jfcd999jBgxgqeeegqAY489lgceeIDzzz+/xjFZWVm89NJLTJs2jeuuu47zzjuPmTNn1thn+fLlDa56IinyzXLodiTs+gZ+WAlH6XckTTNw4MBY6Vk1p9PJv//9b4YNG1bvcR6PB4fDwfXXXw/Ak08+idlsxmw2k5eXx4oVK+jbty9er7dWQLU9aZNBjTfeeIPu3btz3HHHxbY1FNkWEZGWofk4PmOPzuOxCUNZuOJ7NhZXckT3DKaNPJwx6qchIgcYMmQI69atq7W9sLCw3mPGjBnDxo0b63181apVyRiaJJPfDSVfwbBp4N4DW95XUEOSJhQK4fV6ycioPyP0/fff58ILL+SRRx7hZz/7GVB/dn57z9pvkz01li9fzplnnlljW3Vke9myZfTr1w+v18uCBQtSNEIRkc5B83H8xh6dx6vTTuWr2eN4ddqpCmiIiHRmuzZAJAxdDoPcI2Bn7UCWSFNt27aNSCTCWWedRb9+/WrcioqiS8qfcMIJzJgxg8suu4yFCxcSCARi/XwOFA6Hk7pqZ2tLeabGgc3mAP7+97/XuW99kW0REWk+zcciIiJJsntfZk1Wb8jpCxvfTOlwpGNZu3Ytubm5bN68Obbt66+/5thjj6V79+4AmM1mrr/+ekaPHk2fPn24+eabWbx4MRaLBa/Xi91uZ+TIkUA08yM9Pb1Gz7T2pE1maoiIiIiIiLRbe74HZy5YHNHAhnsPeEpTPSrpIP71r39x6qmn1thWUlJCXl5erdXohgwZQnl5OfPnz2f79u0UFhZy+eWXM2rUKAoLCyksLGTr1q3tNqABCmqIiIgkrGB9EeMfXs2AW99g/MOrKVhf1KR9RESk/fmh7AduXXMruz2769+pbDOkR78xJ7N39Oee71t+cNLhbd68mRdeeIErrriixvaSkhIOOuigWvsXFRUxbNgwtm/fDkBVVRXPPfccv/vd73jzzTfZtGlTq4y7JSmoISIikoCC9UVM+funrNtWjicQYt22cq569tMaQYt49hERkfZp1nuzeOW7V7jvk/vq36lsK6R1i/47Y1+PpdLCFh+bdGxer5cJEyYwbNgwzj333BqP7dy5M1Z6sr8ZM2YwefJkevXqBcDcuXOZOnUqQ4cO5fXXX+fKK69slbG3JAU1REREErDwne9qbYtEYOGK7xPaR0RE2p9d7l18vutzujq6snLbSoLhuhsvUrEN0rpG/21NA1smlLb/b8QlddatW8eJJ57Izp07ef7552Pb16xZw6JFi1i4cCH9+/evcczLL7/MihUruPXWW4FoA/h33nmHm266iUAgwOTJk/nkk09iS0u3VwpqiIiIJODb4qo6t28srkxoHxERaX/+t/t/AFzU/yIq/ZV8X1ZHsDoSgcqiaE+NaukHQfm2VhqldES7du2ib9++fPTRR+Tl/bjC2kcffcQf/vAHjjrqKGbMmBHbXlRUxKRJk7jzzjtJT08HYMqUKWzdupVBgwZxxhln8MADD3DRRRdx4403snfv3lZ/TsmS8tVPRERE2pP+3dNZt6281vYjumcktI+IiLQ/G/ZsIMuWxTFdjwHgm9JvOLLLkTV38pRCyA+OLj9uc3aNlqSINNHpp5/O6aefXmv79OnTmT59eq3teXl5vPHGG5xwwgmxbUuWLCE3N5cjjjiixr6nnXYa2dnZSR9za1GmhoiISAKmjurHAY3FAdiwo4K+N73GgFvfoEeWo9Y+BgNMG3l46wxSRERaxMbSjfRO743D7OAg50F8s/eb2jtVlUR/OvcLaqR1i5akiLSiYcOGYTKZatw/MKABMHHiRIzG9hsaUKbGfiKRCF6vN9XDqNf+Y2vL47Tb7bWWEhIR6SjGHp3HYxOGsnDF92wsriTNZmJXpR9/KAyAJxDijfVFjDs6j50VXjYWV3JE9wymjTycMUfnNXJ2ERFpyzZXbubgjIMB6O7szpaKLbV3cu2K/rRn/7jNmQubdrb8AEU6IQU19uP1ehk7dmyqhxGX8ePHp3oI9SooKMDhcKR6GCIiLWbs0XmM3RegGHDrG3Xu8+7GXXw1e1xrDktERFpQJBJhW+U2TugeTefv5ujG5srNtXd07cvUODCo4asAvyvaOFREkqb95piIiIi0AZ5AqM7tbn/d20VEpH3a7dmNL+SjmzO6VGs3Zze2V24nEonU3NG1G4wWsOz3JZ8zJ/qzUkt7iySbMjXq4RrySzC2sZcnEoHqZaOMZuos6k6VcJC0tc+lehQiIq3OYTHVGdhwWk117C0iIu1VkSsakMi1R1c16eroijfkpdRXShf7fv0z3HvAkVXzvXp109CqYshVfyWRZGpjn9rbEKMZTJZUj6IO1lQPQESkUyhYX8TCd77j2+Iq+ndPZ+qofrGSk/39+pS+PLqy9pJ+I47oxviHVzd6vIiItA/F7mIAcuzRrIscW/RnibukdlDDllnz4OqghjI1RJJOQQ0REZEDFKwvYsrfP43dX7etnKue/ZTHJgytFZi48cyjAHjm/ULc/hBOq4kRR3TjjfVFcR0vIiLtQ7G7GIvRQrolHfgxuFHsKuaoLkf9uKN7b+2ghjUtWpJS3URUOpTtZR5KXf5Wu15OmpVe2c3vYbhp0ybsdjs9evRIwqhSR0ENERGRAyx857ta2yIRWLji+zqDEjeeeVQsuAEw/uHVCR0vIiJtX7GrmBx7TmyVv0xrJkaMsQyOGPcesGXU3GYwgCPnx+VepcPYXubhp/etwBsIt9o17RYj/7luZLMDG+effz7jxo3j7rvvrvPxjRs3smjRIm677TYslppVDJmZmXz44YcMGDCgWWNIBgU1REREDvBtcVWd2zcWV7bK8SIi0vbs8uwi25Ydu28ymsiyZVHiPiBQ4dkLmb1rn8CR/ePKKNJhlLr8eANhpo3ql5TsicZsL/PwyDvfUeryN/t6EyZMoKSk/r9Jm83GG2+8wdtvv80///lPevbsGXvMarVit9vrPfa+++5jwYIFbN++vVljjIeCGiIiIgfo3z2dddvKa20/ontGHXsn/3gREWl7dnt2k2GtOY9n2bLY491Tc0dPKXSr49tre2Z0ZRTpkHplOzi0a9tcrverr74iPz8fp9OJyRRtZO5yuXA4HBiNRpYsWRLbNxwO43a7Wb9+PQcffDCrV6/moYceIicnhwkTJvDee+9hNBopKytj1KhRmM1mvF4vixcvZsSIEbHzvPnmm+zYsYMvv/ySn/zkJy36/BTUEBEROcDUUf246tlP2X+VPoMBpo2Mr2N9c48XEZG2Z49nD30y+tTYlmHNYI/nwKBGGdjSa5/AlqWeGpISAwcOZM+emn+nTqeTf//73wwbNqze4zweDw6Hg+uvvx6AJ598ErPZjNlsJi8vjxUrVtC3b1+8Xm+N8hSfz8eqVas47bTTeOutt1o8qGFs0bOLiIi0Q2OPzuOqEYfjtES/zXBaTIwdmMeflm/g0JmvcdjM1xg5/x0K1tfdxX7s0Xk8NmEox/XJxmk1cVyfbB6fMJQx6qchItJu7fbuJvOABqCZ1kx2e/bLvggFwV8F1jqCGvYsZWpImxAKhfB6vWRk1J9B+v7773P44YezdOnS2Da73Y7ZXDsvwm63xzJAAFatWkVeXh4XXnghb775ZnIHXwdlauwnsv9XaqFA6gbSHu33etV4HUVE2qGC9UU1lml1B0I1VjOJAIV73Fz19095bGLdK5qMPTpPTUFFRDqIUDhEua+cTOsBQQ1bJpvKN/24wbuv9LC+oIZ7T+3tIq1s27ZtRCIRzjrrrFoNQFevXk1eXh4nnHACM2bM4LLLLmPBggVceeWVGAyGOoMa4XAYv98f67Hx5ptvcvLJJ3PyySdz00034fP5sNlsLfZ8FNTYj8/ni/077bPFKRxJ++bz+XA6nakehohIk9W1+kldImhFExGRzqDcX044Eq7dU8OaxV7v3h83eMuiP+sKatgywVcR/TLQZKn9uEgrWbt2Lbm5uWzevDm27euvv+bYY4+le/fuAJjNZq6//npGjx5Nnz59uPnmm1m8eDEWiwWv14vdbmfkyJFANPMjPT2dDRs2APDWW29xxRVXcNxxxxGJRFizZg2jR49useej8hMREZED1Ld6SV20oomISMdXti9YcWBQI8OagTfkxRP0RDdUZ2rU1VPDvi/Lw7239mMirehf//oXp556ao1tJSUl5OXlxZYsrjZkyBDKy8uZP38+27dvp7CwkMsvv5xRo0ZRWFhIYWEhW7dujQU0SkpKWLduHTfeeCPdunXD5/O1eAmKMjX2s39KjGvwLxRBTUQoEMtuacnUIhGR1lDf6iV10YomIiIdX6mvFIAMS805P90SDV6U+8pxmB0/ZmpY6sharu7H4d4NGd1baqgiDdq8eTMvvPACL730Uo3tJSUlHHTQQbX2LyoqYtiwYaxbt45evXpRVVXFc889x7/+9S/efPNNjjjiCA499NDY/m+//TZdunThk08+AeCxxx7jrbfeYt68eS32nBTU2E+NqJTJoqBGEx0Y3RMRaW9OObxrXEENA3DK4bmMf3g13xZX0b97OlNH9VM5iohIB1OdqZF+QFlJ9f1Sbyl5aXngrYg+UGdPDWVqdGTbyzxt/jper5cJEyYwbNgwzj333BqP7dy5M1Z6sr8ZM2YwefJkevXqBcDcuXOZOnUqQ4cOZfr06dxzzz28/fbbsf3ffPNNRo8eTd++fQE4//zzueeee9i1axfdunVr8tgboqBGfcLBVI+gtkjkx3EZzdH1AduKtvh6iYg0wYFNQhsy9ug8Hl3x477rtpVz1bOf8tiEupuHiohI+1TqK8WAgTRLWo3t1Zka1Zkc+PYFNSyO2iepztTwKKjRkeSkWbFbjDwSZz+uZLBbjOSkWRM6Zt26dUycOBG3283q1atj29esWcP333/PwoULGTduXI1jXn75ZVasWMG3334LwPLly3nnnXdYtWoVgUCAyZMnM3z4cJ566ikmT54MRDM1brvtttg5TjjhBDIyMnj77be57LLLmvqUG6SgRj3S1j6X6iGIiEgKxNskFGDlxl21tkUiah4qItLRlPnKSLekYzTUbElYnalR7tuX3eetiAY0jKYDTwHWNDAYtQJKB9Mr28F/rhtJqcvfatfMSbPSK7uOwFkDdu3aRd++fXn66afp0qVLbPtHH33EvHnzOOWUU5gxY0Zse1FREZMmTeK+++4jPT36dz5lyhQABg0aRG5uLv369eOiiy7ixhtv5Gc/+xldunRh27ZtNa5rMpkoL4+vpLepFNQQERHZTyJNQj3+UJ3b1TxURKRjKfeV1yo9AbCb7JiN5h9XQPFVRIMXdTEYwZqh8pMOqFe2I+EgQ2s7/fTTOf3002ttnz59OtOnT6+1PS8vjzfeeIMTTjghtm3JkiXk5uZyxBFH1Nj3tNNOIzs7O+ljjpeCGvux2+0UFBSkehj18nq9jB8/HoBXX301tg5wW9NWxyUiEo9EmoQ6rKY6AxtqHioi0rGU+8px1tH802AwkG5Jp8K/r+zEV1l3k9Bq9owfm4mKtHHDhg1r8H61iRMntsZw6qWgxn4MBgMOR9uOsFWz2+3tZqwiIu3J1FH9uOrZT4lEftxmACJ17JthM9cKahgMMG3k4S06RhERaV3lvnLSzHVnYKRZ0qio7qXhqwBLPZkaEM3U8JS2wAhFOi9j47uIiIh0HmOPzuOxCUM5rk82TquJ4/pk8/jEoTw+cSh9c2t++1ZS6cMA9M11/rjvhKGMUT8NEZEOpcxXVmemBkSDGrGeGr7KupuEVrOlgacs+QMU6cQU1BARETnA2KPzeHXaqXw1exyvTjuVMUfnMfboPLIctZf6jgBZTmuNfUVE6rN27VoGDRqEw+FgzJgxlJSUNHpMQUEB/fv3Jy0tjUsvvRS32x17rKKiggsvvJC0tDROOukkvvuu9VZg6EzK/eW1Vj6p5jQ7KfdXNwptJKhhzVCjUJEkU1BDREQkTvU1EVVjUBGJRzgc5sILL+Scc85h48aNOByOOhv07a+srIxLLrmEGTNmsH79egoLC5kzZ07s8T/84Q9kZGSwfv16jjvuOKZOndrST6NTqvBV1BvUqJmpUdFwTw1ruspPRJJMPTVERETiVF8TUTUGFZF4rFy5kr1793L77bdjNpuZNWsWw4cPx+VykZZW9wfmpUuX0rt3b6666ioAbrjhBqZPn86cOXPweDwsWbKEjRs3kpuby5133snHH3/cmk+p06jwNxzU2F61PXrHVwGZPes/kS1djUJFkkxBDRERkTjV2UTUAKccnsv4h1fzbXEV/bunM3VUP8aqDEVEDrBmzRpOPPFEzOboW/BBgwYRCoVYu3Yt+fn59R5zyimnxO6fdNJJbNmyha1bt7J9+3YyMjK47777+POf/8yJJ57ICy+80CrPpTPxhXz4Qj6c5rozMJwW5wE9NRrL1ChL/iAltcq2tm5ZkTMXsvs0+zSbNm3CbrfTo0ePJAwqdRTUaEGRSASv15u08+1/rmSeF6KrqRgMhqSeU0Sko6luIrpwxfdsLK7kiO4ZnHJ4Lo+u+D62z7pt5Vz17Kc8NmGoAhsiUkNRURFdu3aN3TcajeTk5FBcXNzgMcccc0zsfm5uLgDFxcXs3LmTXbt24XK5+OKLL7jiiiuYOXMmTz31VJ3n8vl8+Hy+2P2KiormPqVOodIfLTGst1GoOQ1XwBW943c10ig0HYJeCPrAbEv2UCUVyrbCIydAwNN617Q4YNrHzQ5snH/++YwbN4677767zsc3btzIokWLuO2227BYavYVy8zM5MMPP2TAgAHNGkMyKKjRgrxeL2PHjm2Rc48fPz6p5ysoKNASsSIicRi7r2lotfEPr661TyQCC1d8r6CGiNQSiURq3W/si6X9j6n+t8FgwOVy4ff7mTdvHg6HgyuvvJLrrruu3vPMnTuXO+64oxmj75xiQY0GMjX8YT++oBebv6rxTA2IZmtkdE/ySCUl3HuiAY386yCr+dkTjSrfCqvui163mUGNCRMmNNis2Gaz8cYbb/D222/zz3/+k549fyytslqt2O32Wsf8+te/5plnngEgIyODUaNG8cQTT3DQQQc1a6wNUVBDRESkGdQ8VETi1aNHDzZs2BC7HwqFKCsrIy+v/gBojx492LVrV+z+nj3RFPe8vDx27NiB3W6PfTGVm5sbe7wuM2fOZMaMGbH7FRUV9OnTCh/C2rnGMjUc5ujrX+nehS0SbmT1k319ObzlCmp0NFl9ILdfqkdRp6+++or8/HycTicmkwkAl8uFw+HAaDSyZMmS2L7hcBi328369es5+OCDWb16NQ899BA5OTlMmDCB9957D6PRSFlZGaNGjcJsNuP1elm8eDEjRowA4KqrrmLu3LlUVlZy9dVXc/3117No0aIWe34KarQgu91OQUFB0s4XiURiKYM2my2p5SJ1RdlERKRxah4qIvHKz8/n7rvvJhgMYjab+eyzzzCbzQwePLjBY+bOnRu7/8EHH9C3b1969eqFx+PB5XKxY8cOevbsSXFxcYMBEpvNhs2mkodExZOpAeCq2klXaCSosS9TQ81CpRUNHDiwVsDT6XTy73//m2HDhtV7nMfjweFwcP311wPw5JNPYjabMZvN5OXlsWLFCvr27YvX661RnmKz2cjOziY7O5tf/OIX3HXXXS3zxPbRkq4tyGAw4HA4knZzOp3k5OSQk5OD0+lM6rnVT0NEpGmmjurHgVOowQDTRh6emgGJSJs1fPhwunXrxqxZs9i2bRuzZ8/mggsuwOl0UlFRQSAQqHXM+PHj2blzJ48++iiFhYXMnz+fCRMmANCvXz+GDh3KzJkz+eGHH3jsscc499xzW/tpdXiNZWpUBzvcrn0ZNeY4ghpqFiopFAqF8Hq9ZGTU/wXM+++/z+GHH87SpUtj2+x2e6zR8f7sdnssA2R/fr+fV199lWOPPTY5A6+HghoiIiLNUN089Lg+2TitJo7rk83jE4YyRv00ROQA1Wney5Yto1+/fni9XhYsWADAsccey2uvvVbrmKysLF566SUWLFjAwIEDOfTQQ5k5c2bs8cWLF7NhwwaOPfZYcnJymD17dqs9n86iwl+BESM2U91ZLtXBDq97d3SDMjWkjdu2bRuRSISzzjqLfv361bgVFRUBcMIJJzBjxgwuu+wyFi5cSCAQIBgM1nm+cDhcYyGLRx99lOzsbDIyMnj//fd58MEHW/T5qPxERESkmQ5sHioiUp8hQ4awbt26WtsLCwvrPWbMmDFs3Lixzsf69+/PRx99lKzhSR0q/ZU4LA6Mhrq/D67O1PB59qX3NxTUMNvAaIr21BBJkbVr15Kbm8vmzZtj277++muOPfZYuneP9noxm81cf/31jB49mj59+nDzzTezePFiLBYLXq8Xu93OyJEjgWjmR3p6eqxn0C9/+Utuu+02ioqK+MMf/sD//d//8fzzz7fY81GmhoiIiIiISD1cAVesGWhdbCYbRoMRv6c0usHcQK86gyGareHTcrqSOv/617849dRTa2wrKSkhLy+vVluCIUOGUF5ezvz589m+fTuFhYVcfvnljBo1isLCQgoLC9m6dWuNJsiZmZn07duXYcOGcf/99/PCCy9QVlbWYs8npUGNnTt3ctppp/H555/HtgUCAaZMmUJGRgYDBw6sEXleu3YtgwYNwuFwMGbMmAaXnxERkfhpPhYREalbpb+y3iahsK+PntlBoLqkpKFMDYgGNZSpISmyefNmXnjhBa644ooa20tKSupcdrWoqIhhw4axfft2AKqqqnjuuef43e9+x5tvvsmmTZsavF71MtT1la4kQ8qCGlOmTKFnz568++67Nbbfe++9FBYW8tlnn3HppZfGGiGFw2EuvPBCzjnnHDZu3IjD4WD69OmpGLqISIei+VhERKR+VYEq7A1lXxAtQQlXZ180si8WpxqFdkTlW2HPdy1/K9/a5CF6vV4mTJjAsGHDajUV3rlzZ6z0ZH8zZsxg8uTJ9OrVC4C5c+cydepUhg4dyuuvv86VV15Z6xifz0dZWRkbNmzgtttuY8CAAXTt2rXJ425MynpqzJkzh5kzZ3LooYfW2P7UU0/xz3/+k379+jFjxgwGDhxIOBxm5cqV7N27l9tvvx2z2cysWbMYPnw4LpeLtLS0FD0LEZH2T/OxiIhI/ar8VQ2WnwA4zA5CvkowWaM9MxpiTVP5SUfizI1m56y6r/WuaXFEr5uAdevWMXHiRNxuN6tXr45tX7NmDd9//z0LFy5k3LhxNY55+eWXWbFiBd9++y0Ay5cv55133mHVqlUEAgEmT57M8OHDeeqpp5g8eXLsuMcee4zHHnuMzMxMTj31VP7xj38048k2LmVBja5du9aK1hQVFfHDDz+wcuVKRowYQb9+/XjhhRcwGo2sWbOGE088MbaEzKBBgwiFQqxdu5b8/PxUPAURkQ5B87GIiEj9KgOVjQY17GY7uCobLz2BaKaGV0GNDiO7D0z7GNx7Wu+aztzodROwa9cu+vbty9NPP02XLl1i2z/66CPmzZvHKaecwowZM2Lbi4qKmDRpEvfddx/p6dFVe6ZMmQJE3/vl5ubSr18/LrroIm688UZ+9rOf0aVLF55++mmefvrp5j/HBLSp1U927tyJ0Wjkww8/ZN26dfzxj3/kqquu4j//+Q9FRUU13nQbjUZycnIoLi6u93w+nw+fzxe7X1GhyUNEJB7JnI81F4uISHtW5a+iZ3rPBvdxmB0Y/EXQSPADiGZqVDcVlY4hu0/CQYbWdvrpp3P66afX2j59+vQ6y4jz8vJ44403OOGEE2LblixZQm5uLkcccUSNfU877TSys7OTPuZ4tanVT1wuF6FQiFmzZtG3b19+97vf8c477+D3+4Efm4xUi0Qitbqz7m/u3LlkZWXFbn36tO0/NBGRtiKZ87HmYhERac8q/Y1nakSDGm6wNNJPA8DqVKNQaReGDRuGyWSqcf/AgAbAxIkTMRpTF1poU0GNrKwsgFg6TG5uLpFIhL1799KjRw927doV2zcUClFWVkZeXl6955s5cybl5eWx29atTW+qIiLSmSRzPtZcLCIi7VljS7pCNKhhCnrjy9SwpCmoIZJEbSqo0a9fPywWS6wRSXFxMSaTia5du5Kfn8/HH38cWwrms88+w2w2M3jw4HrPZ7PZyMzMrHETEZHGJXM+1lwsIiLtVSQSwRVsPKjhNDsxBbxgtjV+UjUKFUmqNhXUcDgcnHfeedx+++18//33PPjgg5x55pmYzWaGDx9Ot27dmDVrFtu2bWP27NlccMEFOJ31rxktIiJNo/lYREQE/GE/wXAwrkahllCg8eVcIdooNOiFUCBJoxTp3NpUUANg4cKFRCIRjjnmGIqLi3n44YeBaCO6JUuWsGzZMvr164fX62XBggUpHq2ISMel+VhERDq7Kn8VsG91kwY4zA5soQCReDM1AHyVzR2eiNAGVj85sNncQQcdxFtvvVXnvkOGDGHdunWtMSwRkU5H87GIiEhN7oAbALup8aCGIxwibLJianBPoo1CIdpXw9ml4X1FpFEpD2qIiIiIiIi0RVWBaKZGPOUnjkiEoMnceFDDUp2pob4aHcXOqp2U+lpvmd4cWw490ns0+zybNm3CbrfTo0fzz5VKCmqIiIiIiIjUIe6ghsmOMxwhYDTRaAFKdaaGyk86hJ1VOznvlfPwhrytdk27yc6/zv9XswMb559/PuPGjePuu++u8/GNGzeyaNEibrvtNiwWS43HMjMz+fDDDxkwYECzxpAMCmqIiIiIiIjUwRVwAfH11HBEIgQMjeZpRBuFAniVqdERlPpK8Ya8XHnMlfRM79ni19tRtYMnvniCUl9ps4MaEyZMoKSkpN7HbTYbb7zxBm+//Tb//Oc/6dnzx+dntVqx2+v+7+KLL75g0qRJfP3114wcOZKnnnqKgw46qFljbYiCGiIiIiIiInVIJKhhD4cpMxoaP6kahXZIPdN7ckjmIakeRp2++uor8vPzcTqdmEzRwJvL5cLhcMQawFcLh8O43W7Wr1/PwQcfzOrVq3nooYfIyclhwoQJvPfeexiNRsrKyhg1ahRmsxmv18vixYsZMWIELpeLM888k8svv5yXXnqJ2bNn89vf/pZXXnmlxZ6fghoiIiIiIiJ1cAVcGDFiNVob3M9htGAD4ipAMFrAaFZPDWk1AwcOZM+ePTW2OZ1O/v3vfzNs2LB6j/N4PDgcDq6//noAnnzyScxmM2azmby8PFasWEHfvn3xer2x8pSXXnqJ9PR0/vSnPwFw2223cdhhh+FyuUhLS2uR59fmlnQVERERERFpC1wBF3azHYOh4QwMJ9HHvXEkamAwRJuFKqghSRaORHD5goQPWNHuQKFQCK/XS0ZGRr37vP/++xx++OEsXbo0ts1ut2M2186LsNvtsQyQNWvWcOqpp8Ye69OnD1OmTMHlciX6dOKmoIaIiIiIiEgdXAFXo01CAeyhEBBnpgZEm4Wq/ESSbNteN9/vqqJwt4tIA4GNbdu2EYlEOOuss+jXr1+NW1FREQAnnHACM2bM4LLLLmPhwoUEAgGCwWCd5wuHw3i90b/+7du3k5eXF3vMbDbz2GOPqaeGiIiIiIhIa6vO1GiMKeQDwGNo+BvyGIuCGpJc3kCIMk+ANJuZKl+QKl+QDLulzn3Xrl1Lbm4umzdvjm37+uuvOfbYY+nevTsQDUZcf/31jB49mj59+nDzzTezePFiLBYLXq8Xu93OyJEjgWjmR3p6Ohs2bCAQCMSyNlqLMjVERERERETq4Aq4sJsaD2oYg9GghjsSju/ECmpIkpV5AhgNBg7KsGE1GSl1+evd91//+leNEhGAkpIS8vLyapVaDRkyhPLycubPn8/27dspLCzk8ssvZ9SoURQWFlJYWMjWrVvZsGEDANnZ2ZSWlsaODwQCmM1mvv766yQ+25oU1BAREREREamDK+DCZrY1up8xEE299xCK78QWh4IaklRV3iAOqwkDBtJsZiq9dffW2Lx5My+88AJXXHFFje0lJSV1logUFRUxbNgwtm/fHr1OVRXPPfccv/vd73jzzTfZtGlTjf0HDx7Mxx9/HLv/ww8/EA6HaywHm2wqPxEREREREalDvJkapqAHgKpIAkENb3lzhiZtzI6qHSm7TjgcweMPkZseXaXHaTVR6vbj8YdIs/34kd/r9TJhwgSGDRvGueeeW+McO3fujJWe7G/GjBlMnjyZXr16ATB37lymTp3K0KFDmT59Ovfccw9vv/12bP9f/epXzJs3j/nz53PRRRdx0003ccYZZ5CZmZmU518XBTVERERERETq4Aq4cFqcje5XXX7iCtfdSLEWixMqtjdnaNJG5NhysJvsPPHFE612TbvJTo4tJ3bfGwwRIYLNHC3EsJqNGA0GXP5gLKixbt06Jk6ciNvtZvXq1bFj16xZw/fff8/ChQsZN25cjeu8/PLLrFixgm+//RaA5cuX884777Bq1SoCgQCTJ09m+PDhPPXUU0yePBmA3r17s3z5cqZNm8Ydd9zBT3/6UxYtWtSir4eCGiIiIiIiInVwBVx0sXdpdL/q8pNKAvGdWKufdBg90nvwr/P/RamvtPGdkyTHlkOP9B6x+95ACAPRYAaAAQM2sxG3LwT7Vm3dtWsXffv25emnn6ZLlx//pj/66CPmzZvHKaecwowZM2Lbi4qKmDRpEvfddx/p6ekATJkyBYBBgwaRm5tLv379uOiii7jxxhv52c9+Fjtvfn4+//vf/1ryJahBQQ0REREREZE6uIPuuFY/qc7UqNy3Ckqj1Ci0Q+mR3qNGkKG1eQNhzCYjBn5s8mmzmKjy/Zg5dPrpp3P66afXOnb69OlMnz691va8vDzeeOMNTjjhhNi2JUuWkJubyxFHHFFj39NOO43s7OwkPJOmUVBDRERERESkDu5AvEENLwGDCW8iQQ2/ghqSHL5ACIup5hogNrORMneYQChc67F4DRs2rMH71SZOnNik8yeLVj8RERERERGpgzvoxmZqfPUTU8BD0GTCG0wgqBH0QbD+ZTdF4uULhrGaai7FWt1fw+OPs3ltO6aghoiIiIiIyAGC4SC+kC/u8pOg0Ywv5I3v5BZH9Ke/qhkjlFSI1LFMaiqFIxECoWj5yf7MRgNGgwFvoO0GNZL1WiqoISIiIiIicgDPvmVa41nS1Rj0EjJZEis/AfXVaEcsFgsAbrc7xSOpKRAME4E6SkwMWM1GvIFwKoYVl+rXsvq1bSr11BARERERETmAK+AC4g1q+AgbLQTDQYLhIGZjIx+zqjM1FNRoN0wmE9nZ2ZSUlADgdDoxGAyNHNXyXL4AkaCfcNCIL1wzsGEKB3C5/XjT2lYuQyQSwe12U1JSQnZ2NiaTqVnnU1BDRERERKSVrF27lkmTJvHNN9+Qn5/Ps88+y0EHHdTgMQUFBVxzzTVs376dc889l7/+9a84ndFv+vPy8iguLo7tO3ToUD755JMWfQ6dhTsY/RbZZm68p4Yx6CVssgAhvCEf6fEGNVR+0q7k5eUBxAIbbYHLF6TMHSBcYeXAEIsnEMblCxKptLeJAMyBsrOzY69pcyioISIiIiLSCsLhMBdeeCG//OUvWbZsGdOmTWP69Ok899xz9R5TVlbGJZdcwt133824ceO49NJLmTNnDnPmzIk9vmHDhtgHA7NZb++TxR2IBjXizdQIVQc1gl7SLWkNHxArP1FQoz0xGAz06NGDgw46iEAgkOrhAPC31ZtY9r8S/njOwFqPbSyv4vF3v+evvz6BQ3Ib+ZtsZRaLpdkZGtU064mIiIiItIKVK1eyd+9ebr/9dsxmM7NmzWL48OG4XC7S0ur+wLF06VJ69+7NVVddBcANN9zA9OnTmTNnDj6fD5/Px2GHHYbVam3Np9IpxIIa8TQKDXgJmqyAN75mobHyk4pmjFBSxWQyJe0DeXN9t9dPwGAGU+05oFt2OtsrQ2wqDXBkr8b/jturtlVcIyIiIiLSQa1Zs4YTTzwxlk0xaNAgQqEQa9eubfCYU045JXb/pJNOYsuWLWzdupWysjLsdjuXXXYZDoeDESNGsH379hZ/Hp1FYj01vET2lal44glqVAdKVH4izbSjzEOOs+6gZpbDQprVxA+7O/bfmYIaIiIiIiKtoKioiK5du8buG41GcnJyavTEaOyY3NxcAIqLiykrK8Pr9fLTn/6Ur776CqPRyPXXX1/vuXw+HxUVFTVuUr9YTw1T4z01TAEv7AtqeINxBDWMJjA71ChUmq2owktuWt1BDYPBQI9sB9+XuFp5VK1LQQ0RERERkVYSiURq3W+sgd/+x1T/22AwcNhhh7Fjxw6mTp3KoYceytVXX80777xT73nmzp1LVlZW7NanT59mPJOOzx10Y8CAtY60/gMZg14M+4IfvmC8y7o61FNDmq24wktOPUENgB6Zdr7f1bH/zhTUEBERERFpBT169GDXrl2x+6FQiLKysga7/x94zJ49e4DoKgwWi4UePXrEHsvJyWkw+2LmzJmUl5fHblu3bm3O0+nw3AE3dnN8q0YYgz6wREtK4uqpAWB1gl+ZGtJ0Vb4gbn+ILg0ENfKy7BTuVqaGiIiIiIg0U35+Ph9//DHBYBCAzz77DLPZzODBgxs85r333ovd/+CDD+jbty+9evXikUceYfTo0bHHtmzZQt++fes9l81mIzMzs8ZN6ucOuuPqpwH7ghomGyaDEW+8mRoqP5FmKq6IBtCy6+mpAdAjy06ZJ0CZ299aw2p1CmqIiIiIiLSC4cOH061bN2bNmsW2bduYPXs2F1xwAU6nk4qKijqXiBw/fjw7d+7k0UcfpbCwkPnz5zNhwgQARo4cyXvvvceyZcv45ptvePDBB/n1r3/dys+q4/IEPHH104BoUCNismI1WfHGm6mh8hNpppKKaAAtx2Gpd5+8rOhKO4V73K0yplRQUENEREREpBUYjUaWLFnCsmXL6NevH16vlwULFgBw7LHH8tprr9U6Jisri5deeokFCxYwcOBADj30UGbOnAnA0UcfzWOPPca0adPIz8/n9NNPZ/r06a36nDoyd9Ad13KuRCIYQ34iJgtWozX+8hOzXaufSLOUVDaeqZGXGf0b7sglKOZUD0BEREREpLMYMmQI69atq7W9sLCw3mPGjBnDxo0b63zs17/+tbIzWog74I4rU8MQDmCIhAmbrFhMFryhBBqFerUCjTTdrkofNrMRu6X+XAWH1US2w8JmZWqIiIiIiIh0Hq6AK66ghnFfD42w2RrN1IhnSVeIBjXUKFSaYXeVn2ynpdFmtgdl2ti8p+NmaiioISIiIiIicgB30I3NHEdQIxANakSMZiwma/yNQi1O9dSQZtlV6SOrgX4a1bpn2ClUUENERERERKTziD9TI5qZETZbsRgt8ffUsDjUU0OaZU+Vj0x740GNgzJtbN6r8hMREREREZFOI94lXavLT6pXP/EEPfFdwKyghjTPrqo4MzUy7eyp8uPyBVthVK1PQQ0REREREZEDuANxlp9U99QwWbAaLfjibRRqdULAA+FQc4Ypndgel5/MOIMaANtK4wy4tTMKaoiIiIiIiBzAG/QmVH4SMUXLTxJa/QSUrSFNEolE2Fvlj6v8pFtG9O94SwctQVFQQ0RERERE5ADuYHxLutbI1DBZ4++pYd4X1FCzUGmCKl8QfyhMpsPc6L7ZDgs2s1FBDRERERERkc4gFA7hC/ni6qlhimVqWLCYrPiC/vguokwNaYa9rujfWTyZGgaDgW4ZNrYqqCEiIiIiItLxVTf7TKinhtGC1RjN1IgQafwiFmVqSNPt2RfUyLA3nqkB0C3dxtZSBTVEREREREQ6PHcw+uEv3tVPwgYTGE1YTBZCkTDBeJp/WpzRn/7K5gxVOqm9VfsyNeJoFAooU0NERERERKSziGVqxNkoNGK2AmA1Rj9g+oJx9NVQpoY0w173vkwNW5yZGhk2tpV6iETiyCJqZ1Ia1Ni5cyennXYan3/+eWzbsGHDMBgMsVvXrl1jj61du5ZBgwbhcDgYM2YMJSUlKRi1iEjHo/lYRETkR+5A9BvteBuFhk37ghr7fsa1Aop6akgzlLr8OK0mzKb4PtJ3S7fh9ococwdaeGStL2VBjSlTptCzZ0/efffdGtvLysooKCigtLSU0tJSfvjhBwDC4TAXXngh55xzDhs3bsThcDB9+vRUDF1EpEPRfCwiIlJTdflJvD01IqZohobVGA1qxLUCitECRhP4VH4iiSt1B8iIo0lota77lnXdXuZpqSGlTMqCGnPmzGHTpk21tpeXl3PooYeSnZ1NdnY2mZmZAKxcuZK9e/dy++2307t3b2bNmsXSpUtxuVytPXQRkQ5F87GIiEhNTc3UsOwLbnjiCWoYDNG+GsrUkCYodfnjbhIK0UwNgG0dsFloyoIaXbt2pW/fvrW2l5WVcdttt+FwOBg0aBDr168HYM2aNZx44omYzdFf3KBBgwiFQqxdu7bea/h8PioqKmrcRESkppaejzUXi4hIe1PdUyPeRqHVmRqWfZka/njKT2BfUENfCkji9rr9pMfZTwOiq6TYzEa2lSpTo0X5/X68Xi/9+vXjq6++YuDAgVxxxRUAFBUV1ajnNhqN5OTkUFxcXO/55s6dS1ZWVuzWp0+fFn8OIiIdQTLnY83FIiLS3lSXn1T3yGiIMeglXB3U2PfTG4wzqGG2q1GoNEmpyx93k1Ag2h8t3dYhy0/ifxVagcViYdu2bfTq1QuA3//+9wwbNgyPJ/rCH9ipNRKJYDAY6j3fzJkzmTFjRux+RUWF3kyLiMQhmfOx5mIREWlv3AE3ZqMZs7Hxj0vGoI+IsQk9NSDaLFRLukoTlLr9dOuekdAxXdOtbO+AmRptKqhhMBhib6ABcnJyAKisrKRHjx5s2LAh9lgoFKKsrIy8vLx6z2ez2bDZGq+DExGRmpI5H2suFhGR9sYddMdVegI1MzXMRjNGDMrUkBZX6g4kVH4C0DXdpvKTlrZs2TIOP/zw2P0tW7bgdDrp1q0b+fn5fPzxxwSDQQA+++wzzGYzgwcPTtVwRUQ6LM3HIiLSmXmCHuzmOIMagR97ahgAi8mKL+6eGg711JCERSIRKjyBhBqFAuSm29jRActP2lRQ46STTqKkpIQnn3ySTZs2MXfuXH71q19hMBgYPnw43bp1Y9asWWzbto3Zs2dzwQUX4HQ6Uz1sEZEOR/OxiIh0Zu6AO65+GgDG0I+rn0C0BMUXjLP8xGzXkq6SsEpfkGA40oRMDStlngBuf7CFRpYabSqo0a1bN1588UXmz5/PkCFD6NWrF/fccw8QbUS3ZMkSli1bRr9+/fB6vSxYsCDFIxYR6Zg0H4uISGfmCXriLz8JeGOZGhBtFuoL+eO7kMWpoIYkrNwdACDdbmlkz5py9y3ruqMszqBbO5HynhoHNps766yzOOuss+rcd8iQIaxbt641hiUi0uloPhYREYlyB93YTPH1gzIGD8jUMFkSbBSqnhqSmFJ3NGiWcKZGWvTvdEeZh34HpSd9XKnSpjI1REREREREUs0T8CRQfuKvmalhtOBNqKeGghqSmLLqTI0Egxpd0qwYgJ3lHauvhoIaIiIiIiIi+3EFXAlmauwf1LDiDcb5oVFBDWmCpmZqmE1Gsp2WDld+oqCGiIiIiIjIfuIuP4lEMAZ9RIxNzNQwOyDog1CgiSOVzqjcE8BsNGC3JP5xPjfdqkwNERERERGRjizeoIYhFMBAhLB5/54a1sR6aoCyNSQh5e4A6TYzBoMh4WO7OG3K1BAREREREenIPAEPdnPjq58Y92Vk7J+pYTVZ8AXjXf1kX1DDp6CGxK/MEyAtwdKTal3SlKkBwKuvvlrvY3/961+bPBgREUmM5mMREZHk8wQ9cWVqGAPRoEb4gEah8WdqOKM/lakhCSjbl6nRFLnpVorKlanBfffdV2tbJBLh448/5oUXXmj2oEREJD6aj0VERJIv7qBGMPrhcP8lXaONQhNY/QSUqSEJKfcEcNpMTTq2S5oVlz9Ehbfj9HGJK6iRlZXFIYccQs+ePfnjH/9ITk4Or776Kj179uTggw8mOzub7777jmuuuYb09I6z3q2ISFuj+VhERKRlBcNB/GE/NnM8QY195Sem/ctPrPgSWdIVwF+Z8Dil8ypz+0m3Nr38BKC4A2VrxBXU+MlPfkKfPn34z3/+g8/nw2azEQqFePzxx+nTpw/XXnstVqsVu73xujMREWk6zcciIqnz7bffpnoI0go8+5ZjjS9To+7yE3/YT5hI4xczK1NDElfenJ4azmhQo6iikwU1rFYrVquV7t2719jepUsXrFZr7NvApnRfFRGR+Gk+FhFJneOOO47Bgwczb948Nm3alOrhSAupDmrYTXE0Ct1XflIzUyP6b38ojmahsUwNV4KjlM6sOY1Cc/ZlauzsbJka9amoqCAQCOB2uykrKyMQCBAIdJzaHBGR9kLzsYhIy9u9eze33HILX375JUOHDuXEE09kwYIFbN26Ne5zrF27lkGDBuFwOBgzZgwlJSWNHlNQUED//v1JS0vj0ksvxe1219pn0aJFGAwGCgsLE3lKUgd3IPr6xpWpsS9wUbOnRjSoEVezUJMFjGY1CpWEVHgCpDexp4bFZCTLYel85Sd1iUQi/PrXv+bTTz/lwQcf5Mwzz+STTz7hk08+Seb4RESkEZqPRURaR1paGhdddBHPPvssJSUlXHnlldx555307duX/Px83nvvvQaPD4fDXHjhhZxzzjls3LgRh8PB9OnTGzymrKyMSy65hBkzZrB+/XoKCwuZM2dOrX3+8Ic/NPv5SVRC5SeB6kyNH4Ma1n3/9gQTWAHFp54aEh9vIIQvGG5ypgZE+2p0uvKTuhgMBl5++WWGDRvGzJkzef/99zn55JM5+eSTkzk+ERFphOZjEZHWs3HjRubNm8cpp5zC//3f//HTn/6UF154gSuuuIKLLrqowWNXrlzJ3r17uf322+nduzezZs1i6dKluFz1lx4sXbqU3r17c9VVV9G3b19uuOEGnn322Rr73HzzzZxyyilJeX4C7uC+TI0EGoXW7KkRDWr4E2kWqkwNiVO5J5qJm9bERqEA2U5LhwpqNP2VEBERERHpRI455hi+//57xo4dy/Tp0znvvPNIS0sDYPPmzXTr1q3B49esWcOJJ56I2Rx9Cz5o0CBCoRBr164lPz+/3mP2D1icdNJJbNmyha1bt9KnTx8+/fRTlixZwnvvvccrr7zS4PV9Ph8+348ftCsqKuJ52p1OYo1CvYQNJjD+WArwY/lJAkENNQqVOMWCGs3J1HBaO1T5SVyvRHl5OQaDgQ8//LDG9i+//JLy8nI2b97cIoMTEZGaNB+LiKTOjTfeyPjx48nIyKj12CGHHMK6desaPL6oqIiuXbvG7huNRnJyciguLm7wmGOOOSZ2Pzc3F4Di4mJ69erF1VdfzV133dVoQAVg7ty53HHHHY3u19lV99SIq1FoyF+jSSj8WH7iDSpTQ5KvIhbUaFpPDYBsp5X/bS9P1pBSLq7yE7PZjNVqZc6cORx88MFUVlaSk5PDokWLyMjI4LPPPiMSieDxeIhE4li6SEREmkTzsYhI6kyYMKFWQCMSiRAKheI+x4FzcyQSaXTFqv2Pqf63wWDg8ccfx2AwcOWVV8Z17ZkzZ1JeXh67JdLgtDOpztSw7tcnoz7GoI+wueZ+1aufxNUoFKLLuqqnhsQpGZkaOWkW9lT5CIbCyRpWSsX1Snz00Uc17i9evJhRo0axZs2a2LZwOMxjjz3Gddddl9wRiohIjOZjEZHUmTp1Kvfffz82249lCf/973+ZMmUK3333XaPH9+jRgw0bNsTuh0IhysrKyMvLa/CYXbt2xe7v2bMHgLy8PF566SX+97//0aVLl1iw49hjj2X58uUMHz681rlsNluNsUvd3EE3ZqMZs7Hxj0qmoJeIsWamRnX5SfyZGnaVn0jcktFTI8dpJRyB3VV+8rIaz0hq65rUKPTmm2+ufSKjkUGDBulNtIhIK9J8LCLSeh5//PEaPSkAjj76aLZv3x7X8fn5+Xz88ccEg0EAPvvsM8xmM4MHD27wmP1XVfnggw/o27cvvXr14vnnn2fDhg18/vnnrFq1CoDly5dz/PHHJ/rUZD+eoCeufhoAxkDtTA2TwYTJaEogU8MJfmVqSHzKPQGsJiNWc5PX/CDHGf2bLansGH01mhTeOffcc+t97KyzzmryYEREJDGaj0VEWt6iRYuAaOnH4sWLcTqdsfv//e9/GTJkSFznGT58ON26dWPWrFlcffXVzJ49mwsuuACn00lFRQUOhwOLpea3/uPHj+faa6/l0Ucf5cwzz2T+/PlMmDABoEaGR1lZGQC9e/fGbm//37ymkjvgjqufBkQbhUbqKFOxGi34Qv74LmhxQMW2RIYonVi5J9CsfhoAOc7oPFNcEWc2URsXd3hn06ZN/OY3v2l0v4suuqhGWp2IiCSX5mMRkdb1t7/9jb/97W8YDAaee+652P1FixZhs9l4/vnn4zqP0WhkyZIlLFu2jH79+uH1elmwYAEQLRt57bXXah2TlZXFSy+9xIIFCxg4cCCHHnooM2fOTOrzk5rcQXf8mRpBX43lXKtZjdb4MzXUKFQSEA1qNG8R00y7BaMBijvIsq5xvxpGo5Hnn3+eFStW0K1bN/Ly8jjiiCMYPHgw48aNo2vXrtx6662sXLmS9PT0lhyziEinpvlYRKR1vfPOO0B0/n3ttdfIzMxs8rmGDBlS5yophYWF9R4zZswYNm7c2OB5s7Oz1SA6STxBDzZz/EGNA3tqAFhMFrxa0lVaQIUn2Kx+GgBGo4Fsp5WSyo6RqZHQq3HmmWfy1FNPUVZWxo4dO/jqq6945513uPnmmznkkEPYunUrb731Fn369Gmp8YqICJqPRURSYcqUKWq02Qm4A26sxsZXPoFo+Um9mRrBeDM1nOB3QSQCjayEI1LuCeBsZvkJREtQdnWmnhqTJk3iuOOOw2Kx0KVLF7p06cIhhxyC2+3mvffew+/3Y7PZyMvL48gjj2zpMYuIdFqaj0VEUufRRx9N9RCkFSTUKDToJWyu3X/DYrImlqkRDkDQF10JRaQB5R5/szM1ALIcls7VU6N///6sWrWKFStWMG7cOM4880zOOOMMli9fzoUXXkhhYSFvv/02AwYMYPLkyS09ZhGRTkvzsYiISMtyB9zY6whU1CXaU6N2VofFaMaXSFAD1FdD4lLuCeC0Nj9To9OVn9x0000ArFy5kl27dvHnP/+ZyspKHnjggRr7/fnPf+bwww9n+fLl6rovItICNB+LiIi0LHfQTZYtK659jQEvkTrKTyxGSwLlJ/uCGr5KSOsa7zClk6rwBpvdKBQg22Fh/Y7yJIwo9eLK1Jg6dSpvvfUWl1xyCccffzxpaWncfffdADz99NPs2rWL3bt3M2rUKG666Sa6dtV/jCIiLUHzsYhI6zKZTFRUVADRRqEmk6nGrXqbdBzuYCJLutadqWE1WvEG4/wW3KxMDYlfhSeQlPKTbKeFPVV+wuH232A4rldjypQpfPrppzidTp566inOOOMMRowYAcDvfvc77rrrLrKzs8nLy2PGjBktOmARkc5M87GISOv64YcfYqudbNq0KcWjkdbgDiSwpGuo7iVdoz014szUsDqjP7UCijQiGArj9oeS0ig022ElGI6w1+2na3r7boAcV1DjT3/6ExaLhb1791JYWMjGjRu57LLLsNvtHHTQQXz77beMHj2aoqIiPB4PDoejpcctItIpaT4WEWldhxxySJ3/lo7LG/TGvaSrKeAlUldPDZMl/p4aytSQOFV6gwCkJylTA2B3la/dBzXiKj85++yzOfvsswFYsWIFp556Ku+99x59+vShoqKCjRs3snfvXkaPHs1tt93WogMWEenMNB+LiIi0LHcwzkyNSBhjyF9v+Ykv3kwNS3WmRkUCo5TOqNwTAEhKpkaWIxrU2NUBmoXGFdTIysqiuLiY7OxsrrzySp588knGjBnDN998QyAQ4OKLL+aII47gjjvuYMmSJZSUlLT0uEVEOiXNxyIiIi0nFA7hC/niCmoYg36AOhuFWk0WfCF/fBetXsZV5SfSiArvvqBGMpZ0dXayoIbH4+Giiy6ioqKC2267jQEDBjBnzhx69OhBZmYmn3/+OfPnz8fhcHDuuefy9NNPt/CwRUQ6J83HIiKp89FHH3HiiScCUFBQQE5ODtnZ2bzyyiupHZgkTXUfjHgahRr3NQKtL1MjGA4SjAQbv6jBGC1BUfmJNKI6UyM9CZkaNrMJp9XE7qr2H9SIK8Rz6aWXArB+/XoAFi5cSE5ODqFQiF/84hcYjUYOO+wwAK6++mrS0tJaaLgiIp2b5mMRkdSZMmUKF1xwAQC33HILc+fOJRKJ8Ic//IHzzz8/tYOTpHAH3ABY6whUHMi4b8nW+pZ0BfAGfaRb4vjIZXUqU0MaVeGJBsmSkakB0b4aHSFTI6FXo2fPngB06dIlerDZHFtKsNrAgQOTNDQREamP5mMRkdb37bffcuWVV1JeXs53333HlClT2LFjB9dff32qhyZJ4gl6AOIsP4kGNerM1Ni3zR/ygSWOLxgsTvXUkEZVeAMYAIc1OctIZzk6YVDjQHfeeSfBYDC2Nnc4HMbn8zF37tykDE5EROKj+VhEpOUdffTRzJ8/H7/fz/DhwwkGgyxevJijjjoq1UOTJHEHo5kadnMi5Sd1L+kK0UyNuFhUfiKNK/cEcNpMGA2GpJwv026hpAMENeLqqVGfP//5zzidTmw2Gzabjfvuu4+cnJxkjU1EROKk+VhEpOX95S9/4f3332f9+vXce++9vPfee9x///3cf//9qR6aJEl1+UkiPTUi5jqWdN1XfhL/CigO8FXGOUrprCo8AdKSVHoCnSxTY9asWVgsFoxGI+FwGKvVyg033ADAjTfeGNvv3nvvjW0XEZHk03wsIpI6gwYN4r333ovdP/LII9mxY0cKRyTJluzyE28ozg+MZgU1pHEV3gBptuQGNfZ0gEahcWVq3HvvvYTDYQKBAKFQiHvvvbfO/QxJSoMREZG6aT4WEUktv9/P1q1b2bJlS+znli1bUj0sSZLq8hObufGghilQHdSoY0lXZWpIC6jwBHEmqZ8GRJd1LXUHCIUjSTtnKsQV5nE6ndx2222x+4888kjs37NnzwYgEongcrmYPXs2N910E1Zr4x2DRUQkMZqPRURS55FHHuG6667D7/fX2G4wGAiFQikalSRTdflJfJka+8pP6lnSFRLpqeEEV0mco5TOqsITwGFJYlDDYSEC7HX56ZbR+N98WxVXpkZ93/hdd911mM1mrFYrdrudW265RRO6iEgL0nwsIpI6t912G/fddx8+n49wOBy7ab7tONxBN2ajGbOx8e9+jUEvEQxE6tjXZDJjwKBMDUmqMk9yy0+yHdGMot3tvASlWa/IZZddhslkitV2B4NBgsGgvhUUEWllmo9FRFpeRkYGo0ePxmKpXW4gHYMn6IkrSwPAGPASNluhji8cjBiwGC2JZWr4tPqJNKzCE6BnVuNNbOOVpaAGHH/88WRkZADRdOdt27bRp08ffvjhh6QMTkRE4qP5WESk5T300EP89re/5fHHH2fgwIGpHo60AHfAHdfKJ7AvU6OO0pNqVpMVb8gT34W1pKvEIdmNQjM7SFAjrvKTcDgca4S0efPmGo/98MMP/PDDD2zatIns7OyE3kDv3LmT0047jc8//7zWY++++y4Gg4EVK1bEtq1du5ZBgwbhcDgYM2YMJSWqOxORzkXzsYhI6lx77bWsW7eOY445hq5du3LYYYfFbtIxuIPuuJqEQrSnRl0rn1SzmhLI1LA6IeSHQJzlKtIpRRuFJi+oYTObcFhM7K70N75zGxbXK+JwODj99NMxGAyEw2F69epV536JdNufMmUKf/nLX+p8LBAIMHXq1BrbwuEwF154Ib/85S9ZtmwZ06ZNY/r06Tz33HNxX1NEpL3TfCwikjpPP/10qocgLcwdcMddfmKqLj+ph9VoxR/vkq4WZ/SnvwosySsvkI7DGwjhD4VJsyWvUShES1B2u9p3pkZcQY2tW7fW+9jGjRuJRCJEIhFCoRAbN27kiCOOaPScc+bMYebMmRx66KG1Hrv//vs56KCD2LZtW2zbypUr2bt3L7fffjtms5lZs2YxfPhwXC4XaWlp8TwNEZF2T/OxiEjqnHbaaTXuB4NBzObkfWsqqecOJq/8xGKy4E00qOGrgLSu8R0jnUqFNwBAWhIzNQAyHWb2VHWCTI36HHrooZx//vkYDAYMBgN5eXmcc845fPrpp6Snpzd4bNeuXenatfZ/sNu2bWPevHmsWbOGk08+ObZ9zZo1nHjiibH/cQwaNIhQKMTatWvJz8+v8xo+nw+f78eJpKKioilPU0SkzWvL87HmYhHpKH744QeuvPJKPvjgAzZs2MC8efP44osveO655zj44INTPTxJAk8ggUahQS9hU/1NYy1GK95gvD01qoMa6qshdavwBAFwJjlTI9NuYXdlJ8jUqMvevXv56KOPkjkWAH7/+98zZcoUBgwYUGN7UVFRjTfdRqORnJwciouL6z3X3LlzueOOO5I+RhGRtqStz8eai0Wko5g8eTInnXQSn376KQCzZ8/mj3/8I1dccQVvvvlmikcnyeAKuBIKajSYqWFsSqaGlnWVurVUpkaWw0JxRfvu5RJXo9BQKMQZZ5wRux8Oh+nRo0fSB/PGG2/w6aefcuutt9b5eCQSqXW/obrxmTNnUl5eHrs1lLYtItIetMf5WHOxiHQUa9euZerUqbH5rmvXrtx666188MEHKR6ZJEtCjUID3kYahVrxBuP8sGjdr/xEpA4Vnn1BjSSufgLRFVB2uzpB+YnJZOLDDz+M3TcajS1SN/3iiy+yc+dOevbsCUB5eTnnnHMOf/nLX+jRowcbNmyI7RsKhSgrKyMvL6/e89lsNmy2+CYlEZH2oD3Ox5qLRaSjGDBgAF988UWs1A/gmWeeqZXRJu2XO+CmV3rdTbgPZAp4CNoy6n3carKy17snvgtbHNGfKj+RepTvC2o4rckvP9lb5W80YaAtiytTA6JvSn0+H6WlpQAt0hRp/vz5fPPNN3z++ed8/vnnZGRk8OSTT3LeeeeRn5/Pxx9/TDAYrSX67LPPMJvNDB48OOnjEBFpyzQfi4ikxsMPP8y0adOorKxkwoQJHH744dx///08/PDDqR6aJIknmEBPjUDD5ScJLelqsoHBBL7y+PaXTqfCG8RkNGAzx/0RPi5ZDgv+UJhKXzCp521NCb0TXrRoEXfeeWe96cjNdWCzOqPRSF5eHunp6QwfPpxu3boxa9Ysrr76ambPns0FF1yA0+lskbGIiLRlmo9FRFpXWVkZlZWV3HPPPQSDQbZv305eXh7nnnsu2dnZqR6eJIk76MZujm/1E1Ow8SVdfaE4y08MBrCmqaeG1KvCEyDNakp6NkWWIxoS2FPlJ9Nef+PbtiyhoMYZZ5xBZWUlTz75JKFQqKXGVCej0ciSJUv4zW9+w3333ceIESN46qmnWnUMIiJtheZjEZHWEQgEmDZtGs888wwQDfoaDAZ2796N0Wjkiy++4J577knxKCVZEsrUCDbSU8NoxRdvo1CINgtVUEPqUeEJkJ7kfhoQ7akBsKfKx6Fdk1/S3BoafVXmzZvHvffeSzgcpm/fvsyYMYMZM2Zw0EEHJWUABzab219ZWVmN+0OGDGHdunVJua6ISHuj+VhEpPX98Y9/ZPny5SxbtozRo0djMkXr2cPhMCtWrOA3v/kNubm53HjjjSkeqTRXIBQgEA4kVH7SUFDDYrIQCAcJRoKYDXF8GLU6watGoVK3Cm8g6cu5ArHsjN1V7bdZaKMFOYcddhh33HFHbAKHht/4iohIy9B8LCLS+l544QUeffRRzjjjjBrzr9FoZPTo0TzyyCM88cQTcZ9v7dq1DBo0CIfDwZgxYygpKWn0mIKCAvr3709aWhqXXnopbrc79tiaNWs45phjyMjI4Oyzz47rfFI3dzD6usYV1IhEMAZ9DffUMEYfi7uvhsWhTA2pV7kngNOS/EyNdJsZowF2VyWQVdTGNBrUuOSSS5g2bRoAr732GoMHD+a+++5r8YGJiEhNmo9FRFrfjh07OPnkk+t9/MQTT2Tz5s1xnSscDnPhhRdyzjnnsHHjRhwOB9OnT2/wmLKyMi655BJmzJjB+vXrKSwsZM6cOQB4vV4uvvhi/vCHP8RWpZo5c2acz0wO5A5Egxrx9NQwBn0YiDTcU2NfwMMX77KuKj+RBpR7AjiSvPIJgNFoiK6A0o6XdU0o1NOzZ0+uuOIKfv7zn6t2UEQkhTQfi4i0jlAoxE9/+tMaWRr7CwaDhMPhuM61cuVK9u7dy+23347ZbGbWrFkMHz4cl8tV7/LcS5cupXfv3lx11VUA3HDDDUyfPp05c+awadMmjj/+eC6//HIAzj77bP7+97834VkK/JipYTfFE9SIBirCDWR1VAc1vPH21bA4wavVT6Ru5Z4g3TPiK41KVKbDwp52nKkRd1AjHA4zePDg2JJ9fr+fpUuXkpmZycCBA+nRo0eLDVJERH6k+VhEpPX87W9/S9q51qxZw4knnhhbinvQoEGEQiHWrl1Lfn5+vceccsopsfsnnXQSW7ZsYevWrQwYMIB//etfAFRWVvLKK69oee1mqM7UsJkb/+BoDOwLasSRqeGNdwUUiwMqdsS3r3Q6FZ4Ah+a2zEpzmXYzezp6pkYkEsHnqxm5OeaYY5g7dy47d+5k586d9OvXj2nTpjF16tR6I9kiItI8mo9FRFrXr371q6Sdq6ioqNZy2Tk5ORQXFzd4zDHHHBO7n5ubC0BxcTF9+vQBYMuWLRx66KEcfvjhvPjii/Wey+fz1fh/SEWFmlLuzxV0AYlmajTQKDTWU8MT3wCsaeDT70TqVuEJkNYCq58AZDgs7Kpsv5kajfbUgOib6AMn9FWrVvHRRx+xdetWSktLueWWW7j//vs599xzW2SgIiKi+VhEpL07sMFzJBLBYDDEfUz1v/c/pmfPnqxevZqsrCxuvfXWes8zd+5csrKyYrfqoIhEJZKpYdqXqRGJK1MjkfITBTWktkgkQqU3iNPaMkGNrHbeUyOuoIbRaOSRRx6p9/GMjAwmTpzIZ599FmtcJCIiyaf5WESk/erRowe7du2K3Q+FQpSVlZGXlxf3MXv27AEgLy+PrVu3smHDBsxmMyeffDI333wzzz33XL3nmjlzJuXl5bHb1q1bk/CsOo7EempEsy8aytSoXv0koUah/qr49pVOxeUPEYpESGuBJV2h/ffUiCuoEa+srCzV8YmItAGaj0VE2p78/Hw+/vhjgsEgAJ999hlms7nB+To/P5/33nsvdv+DDz6gb9++9OrVi9dff53JkyfHHjMYDA2WHdpsNjIzM2vc5EfugBujwYjFaGl03x97atSf1WEymjAZjPEv6WpNg5AfAnEGQaTTqPAEAEhroUyNTLuZUneAUDjS+M5tUFKDGiIiIiIiUrfhw4fTrVs3Zs2axbZt25g9ezYXXHABTqeTiooKAoFArWPGjx/Pzp07efTRRyksLGT+/PlMmDABgNGjR/PZZ5+xdOlSNm/ezCOPPMJZZ53V2k+rw3AH3NhN9kbLgQBMgcYzNQxES1DibxS6bwUc9dWQA5RXBzVaqKdGpsNCBCh1t88SFAU1RERERERagdFoZMmSJSxbtox+/frh9XpZsGABAMceeyyvvfZarWOysrJ46aWXWLBgAQMHDuTQQw9l5syZAPTr149FixZxww03cOyxx9KlSxceeOCB1nxKHYo76MZubrz0BMAY8BDBQMTUcFaH1WjFG4q3Uei+lS3UV0MOEAtqWFum/CTLEf07bq99NVom1CMiIiIiIrUMGTKEdevW1dpeWFhY7zFjxoxh48aNdT528cUXc/HFFydreJ2aK+DCZmq8SShEMzXCZisYGv6O2GqyJlZ+AuArj29/6TQqWjpTwx4Nauyu8tG/e0aLXKMlKVNDREREREQ6vYQyNYLeBvtpVLMarXgTaRQKytSQWqozNZwtlKmR6YgGS/ZUtc9MDQU1RERERESk03MFXHGtfALRTI1IA/00qllMFrzBeMtP0qM/vcrUkJrKPQHsFiNmU8t8fHdYTFhMhna7AoqCGiIiIiIi0uklUn5iDHgIxZOpkVCjUEf0pxqFygEqvEHSW6j0BKIrJ2U5LO22p4aCGiIiIiIi0um5Aq64y0/izdSwmqx44i0/MZqigQ1lasgBKjwBnC20nGu1TLuF3QpqiIiIiIiItE/VS7rGwxjwJL+nBkRLUNRTQw5Q7gmQZmuZfhrVMhxmlZ+IiIiIiIi0V66AC1scgQrYt/pJnJkacffUALCkqfxEail3+1slU2NXpYIaIiIiIiIi7ZI7GH+mhsnvji9Tw2TFG0rgg6LVqUwNqaXMEyCthVY+qZblsLBH5SciIiIiIiLtkzuQwJKuCZSf+OJtFAr7ghrqqSE1RctPWj5TY6+WdBUREREREWl/IpEInqAngSVd3YTjWCnFZrIRCAcJhoPxDcSSpqCG1NIqQQ2HhUpfEF8w1KLXaQkKaoiIiIiISKfmCXqIEEmsp0ac5SdA/M1CrengLYtvX+k0KjxB0lq4p0aWI3r+Pe0wW0NBDRERERER6dRcARcADrMjrv0TKT8B8MRbgmJVpobU5A2E8IfCLb76SZYj+reqoIaIiIiIiEg7Ux3UiKf8xBAKYAwHE8vUUFBDmqjcEwBohZ4a0fPvdrW/FVAU1BARERERkU4tFtSIo1GoMeAGSLD8JM5lXa1p4KuEcDi+/aXDqw5qpLdCTw2A3e1wWVcFNUREREREpFNLJFPD5E8kqBHdJ6GeGkTAXxnf/tLhtVamhsVkJM1mapfLuiqoISIiIiIinVoimRqmQDTrIhzHvtWZGp5EMjVAJSgSU+7eF9SwtmxPDYAsh4U9VcrUEBERERERaVdcwQSCGv7ovokENbyhOD8oKqghByhrpfITgEy7RY1CRURERERE2ht3wI3RYIytVtKQRMpPjBiwGq249/XhaJQ1PfrTUxbf/tLhlXsC2C1GzKaW/+ie6bCwS5kaIiIiIiIi7UtVoAqH2YHBYGh0X1N1o1BL40ENAJvJhi/unhrK1JCayt3+VsnSgGimxi41ChUREREREWlfXAFXXE1CAYx+NxEMhE2NZ3UA2ExWPKF4e2rsy9TwlsW3v3R45Z5AizcJrZblsLBbmRoiIiIiIiLtiyvgwmF2xLWv2e+Klp4Y4vsoZTVZ41/9xGgCi0PlJxJT5gmQZm29oEapK0A4HGmV6yWLghoiIiIiItKpuQKuuJqEAhgDbsKW+PaFaFAj7tVPAKwZKj+RmDJ3AGcrrHwCkO2wEIpEKHW3r2ahrRPyEREREWkhBeuLWPjOd3xbXEX/7ulMHdWPsUfnpXpYItKOJFJ+YvK7CcWZ1QHRnhqJBTXSVH4iMWVuP13S4it1aq4spwWA3VV+ctPj6xnTFiioIZ1CJBLB640z7S/O8/l80Xozm80WV1OpeNjt9qSdS0SkMyhYX8SUv38au79uWzlXPfspj00YqsCGiMSt0l8Zd6aGqbr8JE6JZ2qkqfxEYso8Afp0cbbKtbIc1UENH0eS0SrXTAYFNaRT8Hq9jB07NtXDaFRBQQEOR/yRfxGRzm7hO9/V2haJwMIV3yuoISJxqwpUkWPLiWvfxIMaNvZ698Y/GGs6eErj3186tHJ3oNVWP9k/qNGeqKeGiIiItFvfFlfVuX1jcWUrj0RE2jOXP4FGob4qwpb4v4RKPFNDQQ2JCocjVHhbL6hht5iwW4ztbllXZWpIp2C32ykoKEja+bxeL+PHjwfg1VdfxW6Pv1lUQ5J1HhGRzqJ/93TWbavdUO+I7u0nbVZEUq8qUJVQ+YnfmRv3uaM9NRIog7alQ1lh/PtLh1XpCxKO0GpLugJkO6zsameZGgpqSKdgMBharKzDbrerZEREpAU11Ah06qh+XPXsp0T2W33OYIBpIw9P0WhFpD1KZPUTk7+KcFavuM9t27eka5gIRuLonWZNV08NAaKlJ0CrZWpAtFloe8vUUPmJiIiItFnVjUDXbSvHEwjFGoEWrC8CYOzReTw2YSjH9cnGaTVxXJ9sHp8wlDHqpyEicQpHwriDbpzm+JoxmvyuhMpPbCYbESL4QnF+ULRlRFc/2T9aK51S9dKq6fZWDGrYLZRUtK+ghjI1REREpM2KpxHo2KPz1BRURJrMFXABxJepEYlg9rkIxZnVAdFGoQCegBtHPMvGWtMhHAS/K1qKIp1WmSc1mRpb9rhb7XrJoEwNERERabPUCFREWlp1UCOeTA1j0IshEkooU8NeHdSIt1lodSBDzUI7vbJ9mRpp1tbsqWFpdz01UhrU2LlzJ6eddhqff/55bNuGDRs4+eSTSU9PJz8/n40bN8YeW7t2LYMGDcLhcDBmzBhKSkpSMGoRkY5H87G0Vf271/0tpRqBikiyVPmjwdN4MjVMvmgAJJTg6ieQSFBj3/zmLYv7GtIxlbkDmI0G7JbW+9ie7bRS6vITDIVb7ZrNlbKgxpQpU+jZsyfvvvtuje2/+MUvOO+88/j222856qijuOqqqwAIh8NceOGFnHPOOWzcuBGHw8H06dNTMXQRkQ5F87G0RQXrixj/8Go27KydkZFII9C7X/+aAbe+Qd+bXmPArW9w9+tfJ3uoIglpSlC4oKCA/v37k5aWxqWXXorb/WNq+KeffsqQIUPIzMxk/Pjx7N27tyWH3yFVBaJBjXiWdDXtC4AkEtSw7Ss5iTuoYVWmhkSVuv1k2M0YDHE0mE2SbIeFCLDH5W+1azZXyoIac+bMYdOmTTW2lZaWkp6eznXXXUfPnj352c9+xldffQXAypUr2bt3L7fffju9e/dm1qxZLF26FJfLlYrhSwuLRCJ4PJ42e/N6f1yWy+v1pnw89d0iajAlcdB8LG3N/s1B/ft9U2QzGxNqBHr361/z6Mrv8QRCAHgCIR5d+b0CG5IyTQkKl5WVcckllzBjxgzWr19PYWEhc+bMiZ3vF7/4BWPGjOF///sfJSUl3Hbbba3xVDqURIIaZl800Jpoo1BoQqaGghqdXpk70KpNQgGynRaAdtUsNGWNQrt27UrXrl1rbMvJyWHVqlUA+P1+XnrpJQYPHgzAmjVrOPHEEzGbo0MeNGgQoVCItWvXkp+fX+c1fD4fPt+Pv4yKioqWeCrSArxeL2PHjk31MOIyfvz4VA+hXgUFBVpuVhrV0vOx5mJJVF3NQQGO6pHJq9NOjfs8T79XWOf2Z94v5MYzj2rK0ESaZf+gsNlsZtasWQwfPhyXy0VaWlqdxyxdupTevXvHsuVuuOEGpk+fzpw5c/jhhx/49ttvufXWW0lLS2PixIk8/vjjrfmUOoTq8pO4MjV8+zI1rPGtlAJgMVkwYMAdjLP5osUJGMCtrJvOrsztb9UmoRAtPwEoqfQCWa167aZqs6ufOJ1OcnNzef/99wEoKiqq8abbaDSSk5NDcXFxveeYO3cud9xxR4uPVUSkI2vufKy5WBKVrOag1RkaB3L7694u0tKa8iXdmjVrOOWUU2L3TzrpJLZs2cLWrVtxOp088MADsYDInj179GVGE1QFqjBgiGVUNKQ6UyOR8hMjBuxmG55AnJkaRlO0Wah6anR6pe5AqzYJBchyWDAAJZXK1Gi2jz76iFtuuYVrr72WZcuWAdRKpY9EIg3WF82cOZMZM2bE7ldUVNCnT5+WGbC0mEdGlGEzta0yikgE/Psyoq3GaH13W+ELGZj2bnaqhyEdSHPnY83Fkqj+3dNZt6281vZEm4M6LKY6AxtOq6nJYxNpjqZ8SVdUVMQxxxwTu5+bmwtAcXExxx9/PL///e8BKC8v58knn+SGG26o91zKnKtblb8Ku9mO0dB4Zb7ZV0nIZANjYh+jrCYb7njLTyBagqLyk05vr8tP13Rrq17TZDSQ5bSo/KSpdu3axbZt2xg8eDBDhgzhT3/6E0OGDKG8vJwePXqwYcOG2L6hUIiysjLy8uqvqbXZbNhsjUdcpW2zmSLY2+D7z7b7PUjbCgBJ+5TM+VhzsSRq6qh+XPXspxzYFmhnmYeR899hR5kXk9FAKBxhQI8Mpo7qx9g6emz8+pS+PLry+zq3i6RKol/SHXhM9b/3P8blcnHuuecyePBgrr766nrPo8y5ulUGKuNazhXA5KskbE38XaDNZMMdiLP8BMCaofITocztp29u/KVOyZLjtFJc6W18xzYipUu6Huizzz7j7LPPjt2vnqyNRiP5+fl8/PHHBIPB2L5mszlW4y0dy/7/8/aFwKtb3DdfqO7XUSQRmo8llcYencdjE4bWeiNXUumjcI8bfyiMJxDCHwqzbls5Vz37KQXri2qd58Yzj+Lq0w6PZWY4rSamjjycG8apn4akRo8ePdi1a1fsfjxf0h14zJ49ewBix7jdbsaOHYvT6eSFF17AaKz/7f3MmTMpLy+P3bZu3drcp9QhVPorcVri++Bo9lYQinPf/dlNNjyhBIIatnRlasi+RqGWVr9utsNCSUX7CWq0qUyNE088Ea/XyyOPPML48eNZsGAB+fn5ZGRkMHz4cLp168asWbO4+uqrmT17NhdccAFOZ+tHrqTl7Z8aOe3dnBSOpH3z+Xz6b0SaRPOxpNrYo/PqbRh6oEgEFq74vs5sjRvPPEpNQaXNyM/P5+677yYYDGI2m+MKCufn5zN37tzY/Q8++IC+ffvSq1cvAKZOnUpaWhqvvvpqo1lxypyrW5W/Kq4mobAvqJFAk9Bq1kQzNWzK1OjsgqEwlb4gGa3cKBSizUKLyttPUKNNZWpkZ2fz8ssv85e//IUBAwZQXFzM3//+dyD67eCSJUtYtmwZ/fr1w+v1smDBghSPWESkY9J8LG1BfQ1D65JoE1GRVNg/KLxt27YaQeGKigoCgUCtY8aPH8/OnTt59NFHKSwsZP78+UyYMAGAdevW8corr/Dwww/j8XgoKyujrKyslZ9V+1cZqMRutse1r9lb3qRMDZs5wZ4a1gzwKKjRmZW6o/NBRisv6QrQJc1CsRqFxu/A9PiRI0eybt26OvcdMmRIvY9Jx7L/twiPjCjF1gZ7arRVvtCP2S36NkYSoflY2pr6GobWJdEmoiKpUB0U/s1vfsN9993HiBEjeOqppwA49thjeeCBBzj//PNrHJOVlcVLL73EtGnTuO666zjvvPOYOXMmAK+88grl5eX079+/xjEqP01MpT/+nhpmbwX+9G4JX8NuslHmTaCcRI1CO70ytx+AjBSUn+Q4reyp8hEMhTGb2lQeRJ1SHtQQqcv+za9sJtpko9D2oLHGYyIibVl9DUMPZDDAtJGHt86gRJqpvqBwYWFhvceMGTOGjRs31to+a9YsZs2alczhdUqV/kp6pfeKa1+LtxxPl0MTvobNZMfTlNVPIpG2tcyetJq9ruqgRut/ZM9JsxKOwK4qHz2y2u7yCNUU1BAREZE2qbph6MIV37OxuJKDMmxgMLCjLPrBIBAMYzBAbrqVW175ginPfooB6Jpuw2k1UVzho3/39HpXR5GWUbC+iIXvfMe3xVV6/aVdSDRTI2RNS/gadpMNV0I9NTIh5Ae/K9o0VDqd0n2ZGukpKT+JLiNbXKGghkhS+EIG2toypZEI+MPRf1uNbSuAHn292q5IJILXm7zGQ5FIJNZY1mazJS07xW63K9NFpA0Ye3RerQ/EBeuLmPL3T4HofLyr0h97LEJ0lZRq1aujPDZhqD5Yt4L9fzeg11/ah6pAfI1CjQEvxpC/SUENm8mOL+QjFAlhMsSRgmzbV1Ln2augRidV6g5gANKtKQhqOKNBjaJyD/TJbvXrJ0pBDWnzpr2bneohSBJ5vV7Gjh2b6mE0qqCgAIej7UemRTqjeFdFqdbQ6iiSXHX9bvT6S1sWjoRxBVxxLelq9kZ7/DQpU8Mc7XPmCXhIt8YRpLBnRn+690L2wQlfT9q/vS4/GXYzRmPrf8mWYTdjMRnazQoobb/rh4iIiMh+ElkVpZpWR2kd9f1u9PpLW+UKuAhHwnEFNSz7GncGm5A5YTNFV1dxBeMsQanO1HDvSfha0jFEgxqt3yQUon35ctNs7KxoH0ENZWpIm2S32ykoKEj1MOrl9XoZP348AK+++ip2e3zLgLW2tjiuZP9uW+p30RZfOxGJSmRVlGpaHaV11Pe70esvbVWlPxpwi6enhtlTBkCoCUGN6kwNd7x9NWz7MjW0AkqnVbovUyNVctIs7SZTQ0ENaZMMBkO7Sf232+3tZqxtQUv+bvW7EOm49m8+2T0zseWqDWh1lOYoWF/E3OUb2LzXjQE4uIuTM3/Sg/e+312rGWh9K9aUu/0UrC9SCYq0OYkENSz7ghrBJpWfVGdquOI7wGwHoyVafiKd0l6Xn3Rb6j6ud3FaY4252zoFNUSkQclu7Jls+4+tLY9TjUdFmu7A5pOFe6Ifrg/JdVJS6eOgDBsuf5A9VX4ikdqtpSO0tXbT7ceBr32E6Ov/6MrvY9sObAb62IShzF2+gcI9P34jXbjHrYah0iZV+CsASLM0Hqgwe0qjAQ1j4h+hqstP3IE4gxoGA9izVH7Sie12+aOrfqVIbrqNTze3j0whBTVEpEHtpbEnECtDaYvUeFSk6epsPglkOa2s+MOoGtvHP7y6zvIHNapsmnibsu7fDHTs0XlqGCrtRnWmhsPS+P+jLe5SQramlVJZTVaMGBJb1tWeqaBGJ7bX5ePwbolnBSVLbpqV4gov4XAkJc1KE6FGoSIiItKmJdJ8Uo0qkyuRpqz7v8b6PUh7UZ2pEV/5SWmTmoQCGDFgNztwx1t+AtFmoR6Vn3RWe11+MlPUKBSimRrBcITdVb7Gd04xZWqISNxC54ba3qwRAUL7/m0iWjzfVgTB9O841qIXkQYl0nxSjSqTK5GmrPu/xvo9SHtR4avAZrJhjqOkxOLaTbCJmRoQbRbq8ieQqWHLBNfuJl9P2i+PP4Q3EE5po9Cu6VYAdpR7OSizbTfQV6aGiMTP3AZvFsC+72ZpA+M58CYizTZ1VD8ObEljMNTd/DORfaVxU0f1i3vf/20tY+T8dyhYX1Tn7wFg3X77iLQFFf6KuPppAFhdewhWr0rSBHaTPcFMjUxw7Wry9aT92uOKZkekNFMjLdrPoz00C1VQQ0QaFNm/hX1Qt4Rvdb2OIpKQ6uaTx/XJxmk1cVyfbB6fMJQxdfRmSGRfabpxR+fRN7dmun51E9Gr9jUWvWpE3YGk6n0U2JC2oNxXHl9QIxLB4t5L0N70oIbNZKcq3kahEO2pofKTTmmvyw9ApiN1QY00mwm7xcj20rYf1ND3iCLSIJ/vxzo6lVI0nc/nw+lsvF5XROpW3YAy2ftKw+prFLqzwktWPW+2I0QbgtZa17WOffR7klSr8FfE1U/D5HdhCnoJ2rOafC2H2U6VP/4+Ndgyo0u6RiLUmfokHdae6qBGCstPDAYD3TJsbG8HmRoKaoiIiIhInRpq+NlQAlpjj1fvI5JqFf4KnJY4moTu623RnKCG3WxnlyeBchJ7JoT84KuM/ls6jb1V0aBGRgrLTwC6ptnYWppAH5gUUfmJiDTIZkvd+tgdiV5HEWmP+neve6WHI7pn1PtYPI9X7yOSavGWn1j39bYIOpoT1HBQFUgkU2PftdxqFtrZ7HH5cFpNWM2p/bjeNcPGtr1tP1NDQQ0RaZBB6Y5JoddRpHUVrC9i/MOrGXDrG4x/eLX6NzRRfQ0/N+yoYGe5t97jNuyooNwTqPdxA2reKm1Dua88rvITa9UuIhgINKv8xJHY6ifV2Rlu9dXobPZU+est8WtN3dJtbCt1t/necCo/kU4hEong9db/5itR+58rmee12+1t7sOv3W6noKAgaeeLRCI1+nQ0l9fr5ec//zkAL774InZ7cpacstlsSf1dJGtcItK4gvVFTNnXrBJg3bZyrnr2Ux6bMFQ9HBJU3Xh14Yrv2bCzAn8wDIA/FKaksv653B8KU7gn+uHtoAwbuyp9RIgGMw7JdXLzWQPUvFXahAp/BenWhrOKAKxVJdEmocam9xezm+34w3784QBWYxwfWKsDKFrWtdPZXeVP6con1Q7KsOHyhyhzB8hJs6Z6OPVSUEM6Ba/Xy9ixY1vk3OPHj0/auQoKCnA4HEk7XzIYDIakjsnj8ST1NdtfdXAjGdri70JE4lNXc8tIRI0pm6q68er4h1ezblt5wsf3yHbw0S2nt8DIRJonHAnHvaSrrbKIgCOnWddzmKPvK6r8VXSxx3Gu6uVjVX7S6eyp8pGRwiah1bpmRMunt5V62nRQQ+UnIiIi0qE01NxSmq6+17Uxet2lraoKVBGOhEm3xJGpUVlM0JHdrOs5TNGsTVcwzmVdTRawpitToxPaVeVrE+UnB+0LamzZ27abhaY+/CPSClqyhCKZZQqdoURBvwsRaWn9u6fXmVGgxpTNU9/r2hi97tJWlfuif8/xZGrYK3ZS0ePYZl1v/0yN+A/KBlcCK6ZIh7C7yseRbWDuTLeZcVpNCmqItAXJLqEAcDobbyoltel3ISItbeqoflz17Kc1lhQ1GNSYsrnqel33ZwAOfEivu7Rl1UGNxjI1DKEAFtceAs7cZl3PYWlCUMOWCe49zbqutC+RSKTNNAo1GAx0z7S3+aCGyk9ERESkQ6lubnlcn2ycVhPH9cnm8f9v797jmyzv/oF/kqZtkrZJ07Q0pS0UCwoopzKOgqBOlImAujn2AMPfUOEBNiaehhuieKjII0xUPDzOeQIPbGM4EAEHcujoYJyEAlLoGZq2tE3TpE3SJNfvj9o8lJ5SyOFO+3m/XnnBfbhyf5M7XDTfXtf3mjWchSmv0ZXva5pejbT4qP97j2cPxzuz+b5T6GhKaqjD2//lSERtGWQQaIiKu6brRYRFQi6TdW5ZV6UWsJRf03UptNTUN8DpFpJIagBAQkwkCiu9nDIVJBypQURERF1OU3FL8i1v3le+7xQqqu3VAICY8PaH+StrLgAAHOr4a7qeHDKoFWrUOjpRZ0apBcwXr+m6FFouWRqnVWvV0khqGDRKHC6sDnYY7eJIDSIiIiIi6nZq7DWIkEcgIqz9VR0izRfhlivgVGmv+ZoqhaqTIzViufpJN9O0XLZURmr00ESitKbes6S3FDGpQURERERE3U61rRrREdEdFhlXmkrgiEoAZNf+1anzSQ1t4+onbRWzoS7nksUBQDpJDYNGCbcAiqulW1eDSQ0iIiIiIup2THZTh1NPAEBpKkJD1LVNPWmiUqhQ6zB3okEs4HIA9k60oZBWUWtHpEIOVXhYsEMB0JjUACDpuhpMahARERERUbdTbatGVIQXy7maSuCITvDJNVUKNcydrakBABYu69pdlNfaEKuO6HAEUaDooiIQESZHXgWTGkRERERERJJRbavucDlXeUM9Iq0VsPsoqaEOV3dupIZS1/inlSugdBcVZjtiJVIkFADkMhkMWiUKOFKDiIiIiIhIOqrsVYiJ6GDlE1MJAMAR3cMn11QrVLA4LBDwskaGKrbxTy7r2m2U19olU0+jiUGr5EgNIiIiIiIiKamq9yKpUV0EwIdJjfAoNLidsDlt3jWIiAbkCsDK6SfdRZnZBp26/RV5Ai1Jq0TeJSY1iIiIiIiIJMEt3Khx1EAToWn3PFV1IRqUWrjDVT65rlqhBgDv62rIZIBKB1jKfHJ9kr7yWmlNPwGAJK0Kxhob6hzOYIfSKiY1iIiIiIioW6mx18At3B2O1FBVF8Aek+iz66p/SI50rq5GLKefdBN2pws19Q3QSSyp0VPbuAKKVKegMKlBRERERBQgR44cwdChQ6FSqTBp0iSUl3f8ZXX79u24/vrrERUVhRkzZqCurq7Z8XPnzmHgwIEwmUx+irrrqbZVA0DHSY2qAjiifZfUiPqhMGlNZ5ZoVcWyUGg3UW62A4D0pp/ENibjzldYghxJ65jUICIiIiIKALfbjfvvvx9TpkxBbm4uVCoVHn300XbbmEwmPPDAA1iyZAlycnJQUFCAF1980XP8zjvvRL9+/XD69Gl/h9+lVNoqAbSf1JA5HYisNcIeY/DZdZUKJeQyGWrsNd43UumAWqPPYiDpKjM31lqRWlIjOlKBWHU4znOkBhERERFR97Vnzx5UVVXh2WefRUpKCpYvX45NmzbBam37i8KmTZuQkpKC+fPnIy0tDU8++SQ++eQTz/EPP/wQ+/btC0T4XUpTUkMboW3zHKWpCDLh9un0EzlkiFJEw9zZZV1ZU6NbMP6Q1IiLklZSAwCSY1U4X86RGkRERERE3VZWVhZGjhwJhUIBABg6dChcLheOHDnSbpuxY8d6tkeNGoWioiIUFxcDAAwGA1JSUry6vt1uh9lsbvborirrKxEuD4dK0XYBUFVVIQDA4cOkBgBEhathdnRmpEYsYKkAhJfLwFLIMtbYEKmQQx0RFuxQWkjSqnC2zMsCtwHGpAYRERERUQAYjUbEx8d7tuVyOXQ6HcrK2v4t/JVt9Ho9ALTbpi2ZmZnQarWeR2pqaqefo6uorK+ENlILmUzW5jmq6gI0qGJ9tvKJ53nD1TB1ZvqJOg5wNwD11T6Ng6SnzGyDPiqi3c9lsCTHqlBQaYXT5Q52KC0wqUFEREREFCDiit+2CyE6/AJzeZumv1/Nl56lS5eipqbG82ga7dEdVdoqO17OtaoAdh8WCW0SHR4Nk83kfQNVXOOfrKvR5ZXW2KCT4NQTAEjRqdDgEiisquv45ABjUoOIiIiIKACSkpJQUVHh2Xa5XDCZTDAY2i5EeWWbysrGWhDttWlLZGQkNBpNs0d3danuUodJDXVVvs+nngBAdEQ0ahwm7xuof0hqWJjU6OpKa2ySKxLaJEXXOGIpt0x6dTWCmtQoLS3FhAkTcOzYMc++vLw8TJgwATExMZg4cSIKCws9x65mCSwiIuoY+2MiIv8bP348Dh06BKfTCQA4evQoFAoFhg0b1m6bf/3rX57t7OxspKWlITk52e/xdmXl9eXQRrZdJFTeYENEbZlPVz5pEh0ejRqHGW54WSODIzW6jYumeuijpZnU0KrCEaNUSLKuRtCSGvPmzUPPnj2xd+/eZvsfeeQR9OrVCydPnoRer8fChQsBXN0SWERE1DH2x0REgTFu3DgkJCRg+fLlKCkpwYoVK3DfffdBrVbDbDajoaGhRZtp06ahtLQUb731FgoKCrBq1SrMmjUrCNF3LRV1FYiNjG3zuNJUBBmET1c+aRIdHgWX2wWLw8vfeIeFA5FaoLbU57GQdLjcAhW1duglOv1EJpMhRSfNYqFBS2q8+OKLyM/Pb7bP4XBg165dWLp0KXr37o25c+d6lqi6miWwiIioY+yPiYgCQy6XY+PGjdiyZQv69u0Lm82G1atXAwAGDx6MrVu3tmij1WrxxRdfYPXq1Rg4cCD69OmDpUuXBjr0LsXpdqLaXt3uSA1VtX9WPgGA6PAYAIDJbvK+kTqOIzW6uIpaO5xugbioyGCH0qYUnRrfG6WX1FAE68Lx8fHNKjkDQENDA1555RX06dMHQOOcQZWqce5Oe0tgjR8/PrDBExF1IeyPiYgCJyMjA8ePH2+xv6CgoM02kyZNQm5ubpvH09LSWhQgpbZV2argFu52R2qoqgrgUOngVih9fv2YiMakRrWtGr1ivFyBRhUHmC/6PBaSjgumegBAvESnnwBAqk6F3WfK4XC6EaGQTnnOoCU1WhMVFYXHH38cQOMP1GvXrsXs2bMBXN0SWHa7HXa73bPdndfiJiLqDF/2x+yLiYhISirqGguvtp/UKPTLKA0AiIqIglwmQ5WtyvtGaiY1urqLnqSGdEdqpOrUcLoF8i5Z0N8gnULD0kmvXMbpdGLmzJmQy+VYsWKFZ39nl8DiWtxERNfGF/0x+2IiIpISY13jNI44ZVyb56iq/bOcKwDIIWtc1tVe7X0jtZ5JjS7ugqkeURFhiIqU1LiDZlLj1ACAM6XSmoIiuaSG2+3GjBkzcO7cOWzbts0z3PlqlsDiWtxERFfPV/0x+2IiIpKSMmsZFHIFoiOiWz3etPKJv0ZqAI1TUCrrK71vEBUPWMsBl9NvMVFwlVTXIT5GuqM0ACAqUoH46AickVhdDcklNVasWIFz585h165diIv7v+zp1SyBxbW4iYiunq/6Y/bFREQkJWV1ZYhTxkEua/2rkLKm2G8rnzSJidB0cvqJHhBuwMJioV1VSVW9pKeeNOkVp8bpUmlNJZZUUsNoNGLNmjV46623AAAmkwkmkwlut7vdJbCIiMi32B8TEVFXZbQaO6ynAQAOP00/AQBNhAaXOjVSI6Hxz5oL/gmIgq6oqg4JEh+pATCp0aHt27fDbDZj7Nix0Ol0nkdRUVG7S2AREZFvsT8mIqKu6qLlIvRKfZvHldWFaFDp4A73/conTbQRGlTbq+GGl6vWNCU1zCV+i4mCx+0WuGCqR4+QSGpEobzWjksWe8cnB0jQq5BcXmxuzpw5mDNnTpvntrUEFhERXTv2x0RE1B1ctF7ESMPINo+rqgpgj+7h1xg0kRo43U7U2Guga2fUiEdEFBCu5kiNLqrCYofd6Q6JkRpp+saRuadLzRjfLyHI0TSS1EgNIiIiIiIif2lwN+BS/aV2R2qoqgv8WiQUALSRWgDApfqKDs68TFQPoIbFtruiwso6AEBijP9GB/lKolYJVXgYci5KZwoKkxpERERERNQtlFnL4BZu6FWtJzVkTjsizUa/FgkFgNiIWABARV0nkhrR8YCpyD8BUVAVVloBAIka6Sc15DIZeunVOMWkBhERERERUWCVWBprUiSoWh82rzIV/bDySevLlPtKeFg4osOjOpfUiOrBpEYXVVBpRXx0BCIUofH1PE0fhe9KTMEOwyM03jUiIiIiIqJrVFJbAjnkbY7UCMTKJ020kbEoqy/3vkH0D0kN4WVxUQoZ+ZesMITAKI0mfeLVKKisQ62tIdihAGBSg4iIiIiIuomS2hLEqeKgkLe+XoKqugANqli/rnzSRKeMhdFq9L5BtAFwWID6av8FRUFxrtwCgzZ0khrXxUcDgGTqajCpQUREREREQWFtsOLd797Fb3b9Bn88/Edcqr/k1+sV1Rahh6rtlU1UlQV+n3rSRBcZh/K6Mu8bNMVVne+fgCgo3G6Bwso6JGlVwQ7Faz1jVYhUyCUzBYVJDSIiIiIiCjij1Yif/+PneOf4OyivK8dn33+G+zbfh9OVp/12zbyaPCRGtT21RFWVF7CkRpwyDtaGOtQ6ar1r0BRXFZMaXckFUz3sTjd6xoZOUiNMLsN1CVE4XlIT7FAAMKlBREREREQBVtdQh/k758PqtOK5sc/hsR89hhfHvYjYyFjM/2Y+yqydGMHgJbdwo9hcDENU60kLucOKSEs57DFJPr92a/SqOADARWupdw0iooFILUdqdDG55Y1JrRRd6CQ1gMYpKMeKTMEOAwCTGkREREREFGCv/udVlFhKsDhjsWfkhCZCg8UZiyGEwLKsZRA+Loh5wXIBDrcDSVGtJy3UlY3JArs2MEkNnTIOcshQar3ofSNNElB53n9BUcCdLbNAFR6GuKiIYIfSKekJ0bhgqkd5rS3YoTCpQUREREREgXOs/Bi+OPsFftrvp0iOTm52TBOpwZwb5+BA6QFszd/q0+ueNzUmA3pG92z1uKoqD0ImhyOq7ZobvqSQhSFOGYcLtRe8bxSTBFSe819QFHBnjbVIjVNBLpMFO5ROuT6xsVjokUJTcAMBkxpE1AVkZWXhZz/7GbKysoIdChEREbVDCIGVB1ciTZOGW3vd2uo5gxMGY3iP4VjznzWod9b77NrnTOegUqigi9S1elx96TzsMQaIsNZXRvGHeHUCimuLvW+gTQEu5XJZ1y4kp9SMFJ062GF0mj46EvroCBwpCv5qPExqEFFIs9lsePXVV1FWVoZXX30VNlvwh8ARERFR63YW7sTJypN44IYHIJe1/VXkZzf8DJW2Smw4vcFn1z5bfRbJ0cmQtfEb8ahLubBrAjP1pEmCKgHFtSUQ8DJJoU0BbCbA6t9VYigwHE43zpdb0FsfekkNALg+MQb/zq8MdhhMahBRaPvkk09QWdnYmVZWVmL9+vVBjoiIiK60PceIaW/sx4BlX2PaG/uxPccY7JAoCFxuF9449gZu1N+I/nH92z23h7oHbkm5Be+ffB8Wh8Un1z9TdQa9Ynq1ekzmckJVmQ+bNsUn1/JWYlQi6px1qKiv8K6B9of4K874LygKmO+NtXC6Bfroo4IdylUZYIhBzgUz6hzOoMbBpAYRhaySkhKsX7/eU0hMCIH169ejpKQkyJEREVGT7TlGzPv4MI6X1KC+wYXjJTWY/8lhJja6oe0F25Ffk4/pfad7df6U66ag3lmP9aev/RcWdQ11KDQXopem9aSGsroAcndDwJMaBnVjkdSCmgLvGmiSAHk4UO6/ZW8pcL67YIJcBvQO1aRGkgZOt8DhwuBOQWFSg4hCkhACa9asaXO/ryumExHR1Vm3u2VRQyGAdd9yBYfuxC3cePu7tzEofhDSY9O9aqNT6nBLyi346NRHsDZYr+n6pypPwS3c6KPt0+rxqPLvIWQy2LStFxH1l6jwKGgjtciryfOugVwBxKYCZSf9GxgFxLEiE3rr1YhQhObX8uRYFWLV4cg6F9wpKKH57hFRt1dYWIhDhw7B5XI12+9yuXDo0CEUFhYGKTIiIrrc2bLWpw7kltUGOBIKph2FO5Bfk4+p6VM71W5yn8moc9bh0zOfXtP1v7v0HSLDItEzqvWkRXT5GdhjekIoIq/pOlcjOaoncqs7saKJLg0wfue3eChwDhdWIz0hOthhXDWZTIYbkzTYl+vl9Ck/YVKDiEJS7969MWLECISFhTXbHxYWhpEjR6J3795BioyIiC7XtOzflfolxgQ4EgoWt3Dj7WNv40b9jV6P0mgSp4zD+OTx+CDng2sarXG47DDSY9MRJg9r9Xi08STqda1PTfG3lJhUFJoLYHPZvWsQlw6U5QCuBv8GRn5VabEj75IV/Q2aYIdyTYakxiLnohkVtV5+fv2ASQ0iCkkymQyPPvpom/vbqmxORESBteDWvriyS5bJgIUTO/fllkLXjsIdOF9zHtPSp11V+7v73A2rw3rVK6E43U4cKTuC63XXt3pcUV8DlakY9XFpV/X816pXTC+4hBu51We9axB/PeByNCY2KGRl51UBaKxLEcoGp8RCBmD39+VBi4FJDSIKWSkpKZg5c6YngSGTyTBz5kwkJycHOTIiImpy540GvD1rOIakxkIdEYYhqbF4Z9ZwTLrREOzQKACcbifePPomBsUPQl9d36t6jjhVHG5JuQV/Pvln1NhrOt3+5KWTsDRYcKP+xlaPx5QeBwDU6YOTaItTxUEbocGJS17WydCnN9bWKD7o38DIr/afu4TkWBXioiKCHco10arCcb0hBjuCWPyZSQ0iCmmzZs2CXq8HAMTHx2PmzJlBjoiIiK50540GbF54M06tuAubF97MhEY3svncZhSYC3Bv33uv6XmmpE+Bw+3A+yff73TbvSV7ER0e3WaRUE3JEdijEuBUxV5TjFdLBqCP9jocLT8CAS8KnYdFAPp+QNG//B4b+YcQArvPlGNQijbYofjEiN5x2HO2AmZbcKZEMalBRCFNqVTiscceQ2JiIpYsWQKlUhnskIiIiAiAtcGKtUfXYnTSaKRp067puWIjY3Fn2p345NQnKK4t9rqdEAI7CndgSMIQyGWtfPURAtqig6hLaH1qSqDcoLsel+orUWD2stC54SYgfy/gdvs3MPKLExdqYDTbMLyXLtih+MSYdD2cLoFtJ0qDcn0mNYgo5N18883YuHEjbr755mCHQkRERD9489ibsDZY8dN+P/XJ801Om4zoiGhk/jvT66XbcypzUGguxKikUa0eV1XmIdJSDkviQJ/EeLV6aXohOjwKBy56OfqiZwZQVwmUHvNrXOQf/zh+ERqlIuTraTSJi4rAoBQtPj3ofcLRl5jUICIiIiIinzpecRzrT6/H1PSpiFPF+eQ5IxWRmDlgJvZd2IcteVu8avPZmc+gV+oxUN960iLu/LdwhatgjQ9u4Vq5TI4b9Tdh/4Us71ZB6TEAiIwBznj3PpB0OJxu/O3IBYxNj0eYvOsUtv9x/0QcKzbheLEp4NdmUoOIiIiIiHzG7DDjqb1PIU2Thkm9J/n0uYf1GIYxSWPwQvYLyK/Jb/fcktoSbMnbgtt73d761BO3C/Hf74C555DGwptBlpE4DA6XHbuLdnV8slwB9BoDfPcFp6CEmH8cv4hKqwO3D+gR7FB8anhvHQxaJd7YdS7g12ZSg4iIiIgoQI4cOYKhQ4dCpVJh0qRJKC/veBnE7du34/rrr0dUVBRmzJiBuro6r44FQ4OrAU/seQImuwmPDH4EYfIwn19j1sBZiI2MxYJvFuBS/aVWzxFCYOXBlYiJiMHE1ImtnhOXtxcRdZUw9R7j8xivhiZCg8EJQ7AlbytqHF6s8tL3DqCmGDj3jf+DI59wON1YuysXw3vpkKJTBzscn5LLZbhvWDJ2ni5Ddl5lYK8d0KsREREREXVTbrcb999/P6ZMmYLc3FyoVCo8+uij7bYxmUx44IEHsGTJEuTk5KCgoAAvvvhih8eCwea04bE9j+Gg8SD+e8h/o4faP7+JVilU+E3Gb2BtsOKX236Jc9UtfzP83on38G3Jt5g5YCaUipZFxGUuB5IP/hmWHv1h10pnKfhxyeMgkwF/OvE+XKKDERgJ/Rsf377E0RohYt2351BcVYefj0gNdih+cXPfeNyQGIMn//JdQFdCYVKDiIiIiCgA9uzZg6qqKjz77LNISUnB8uXLsWnTJlit1jbbbNq0CSkpKZg/fz7S0tLw5JNP4pNPPunwWKCdrT6L2dtmI+tCFhYOXdhmDQtf6aHugadGPgW3cOOBLQ/ghewXsLdkL3YX7cZvd/8Wa4+uxfT06RieOLzV9inZ7yHCUobyAXf7Nc7OUitUuLvP3cipPIk/nfwTHO52vhjKZMDw/wdcPAoceD1wQdJV2fpdKdb+Mxf3DktGalzXGqXRRC6T4b8npqPSasfDH/4HFrszINcN/uQxIiIiIqJuICsrCyNHjoRC0fgj+NChQ+FyuXDkyBGMHz++zTZjx471bI8aNQpFRUUoLi5u91hqqv9/E2x32XHYeBh/P/d3bC/cDoPagKdHPY1eml5+vzbQmNhYNnoZvi74GtsLtuPz7z/37H9k8CMYnTS6RZswuwXJB99HYs6XMN50LxyapIDE2hl9tH0w5bp78FX+V8ivyce09GkY1mMoIsMiW56ceCNw00+Bnc8ATgcw9tdAOJe3l5LyWhve2ZOH9/fnY2xfPe7LSAl2SH6VqFHi8Uk3YNX273H32n14+icDcHv/HlCE+W88RbdKajQt/WQ2m4McCRFRc039krdL1IUy9sVEJFX+7ouNRiPi4+M923K5HDqdDmVlZe22GTRokGdbr9cDAMrKyto91lpSw263w27/v5U1amoa6zZ0tj/OPJiJzec2N9sXr4xHhjYDx4qP4RiOder5fOGWhFtQY2t8PbHKWJRXlePLqi+bnXOD8SymntwGACiOjMZ54znAGPiiht7QAZjeoISx4hxyil5FDoB+un4YGj+4lbMjAWcE8PXzjY+53wA9+gc4YrpSg8uNn79zAGfLLAAArVKBJJXA5oPS/Mz52o96RuLb7y/h4ff2AQDm3XIdfn17P6/bd6Y/7lZJjdraWgAISOaaiOhq1NbWQqvVBjsMv2JfTERS58+++Mof0IUQkMnaX9bx8jZNf29q096xK2VmZuK5555rsd9X/fE+7PPJ8wRGLYDSYAfRSUbAm/f45VF+j4Q6rxjAyWAHEUR/+CPwh6to501/3K2SGj179kRxcTFiYmI6/M9DqsxmM1JTU1FcXAyNRhPscLo13gvp6Ar3QgiB2tpa9OzZM9ih+F1X6IuBrvG56wp4H6SjK9wLf/fFSUlJOH36tGfb5XLBZDLBYDC026aiosKzXVnZuKqAwWBo91hrli5diiVLlni23W43qqqqoNfrW+2PQ+meMlb/YKz+E0rxBiPWzvTH3SqpIZfLkZLSNeYwaTQayX/4uwveC+kI9XvR1UdoNOlKfTEQ+p+7roL3QTpC/V74sy8eP348Vq5cCafTCYVCgaNHj0KhUGDYsGHttsnMzPRsZ2dnIy0tDcnJye0ea01kZCQiI5vXZYiNje0w7lC6p4zVPxir/4RSvIGO1dv+mKufEBEREREFwLhx45CQkIDly5ejpKQEK1aswH333Qe1Wg2z2YyGhpYrXUybNg2lpaV46623UFBQgFWrVmHWrFkdHiMi6i6Y1CAiIiIiCgC5XI6NGzdiy5Yt6Nu3L2w2G1avXg0AGDx4MLZu3dqijVarxRdffIHVq1dj4MCB6NOnD5YuXdrhMSKi7qJbTT/pCiIjI7F8+fIWQwcp8HgvpIP3goKBnztp4H2QDt4L72RkZOD48eMt9hcUFLTZZtKkScjNze30sWsVSveUsfoHY/WfUIpX6rHKRHdYP5CIiIiIiIiIuhxOPyEiIiIiIiKikMSkBhERERERERGFJCY1JCI/Px+lpaXBDoPAeyElvBcUDPzcSQfvhXTwXhARkVQxqSER06dPxx//+Mc2j+fm5mLZsmWtLvWl0Whw+vRpP0bXvfBeSAfvBQUDP3fSwXshHbwXXVdpaSkmTJiAY8eOebX/yJEjGDp0KFQqFSZNmoTy8vKgxpqXl4cJEyYgJiYGEydORGFhoWRjPX36NMaMGYPo6GiMHz++WYFXqcXaZO/evZDJZPj2228lG+vo0aMhk8k8j/j4eMnG2tDQgHnz5iEmJgYDBw7EwYMHJRlrQUFBs/e06SGFWFvDpIZEzJo1C263u83jkZGR+Prrr3HLLbfg4sWLzY5FRERAqVS22fbVV19FcnKyz2Lt6vxxLx588EFPZ6DRaDBt2rSg/+MPBf76d3HixAmMGDECMTExuOeee3gvqBn2x9LB/lg62B93TfPmzUPPnj2xd+9er/a73W7cf//9mDJlCnJzc6FSqfDoo48GNdZHHnkEvXr1wsmTJ6HX67Fw4ULJxvpf//VfmDp1Ks6ePYv+/ftj/vz5ko0VaPwCvmDBgmb7pBiryWTC9u3bUV1djerqauTl5Uk21v/5n/9BQUEBjh49ihkzZmDWrFmSjLVXr16e97O6uhofffQRDAZD0GNtk6CAysnJEXFxcSIlJUX07t1b9O7dW8THx4vU1FTPdtMjNTVV6PV6YTQahRBC2Gw2sWrVKlFXVydmzpwp+vTpI9LT00VYWJjo3bu3SE9PF8nJyWLPnj3Nrjlp0iQBQJw4cSIYL1myAnkv5syZI+bPny+qq6tFUVGRuPvuu8Xs2bOD+fIlJZD3wmKxiOTkZLF06VKRl5cnHnzwQTFt2rQgvnoKFvbH0sH+WDrYH3cvFRUVIj8/XwAQR48e7XD/rl27hEajEQ0NDUIIIQ4fPixUKpWwWCxBidVutwuZTCZycnKEEEJs3bpVaDQaScZaVVUlxo0bJ+x2uydWg8EgyVibrFy5Utx6661Cq9WK3bt3SzZWg8Egzp492+J8Kcaanp4ujh07JoQQora2VmzcuFG4XC5Jxnq5hx56SDz44INCiOC+r21hUkMCVCqVOHDgQLvn1NXVNduur6/3fJASExNFfn6+Z7/T6fScZ7PZhEqlEhMmTBCrV6/2beBdkL/uxZw5c8TixYs9bdavXy8GDBjgu8C7IH/di/fff1/ccMMNnjZ5eXkCQFA7YpIO9sfSwf5YOtgfd31tfZm5cv/zzz8vfvzjH3u2XS6XiIiIEHv37g1AlC1jslgsnkSaEEJ89NFHIjExUZKxXs5ut4s5c+aIyZMnCyGkGWtxcbHQ6XTi1KlTzZIaUoxVqVSKGTNmCKVSKYYMGSJOnjwpyVhLS0uFTCYTr732mtBoNCIjI8OTjJFarFdKTk4WX3zxhRBCGrFeidNPgszlcsFmsyEmJqbNcw4cOID09HRs2rTJs0+pVEKhULQ4V6lUIiwszLO9b98+GAwG3H///dixY4dvg+9i/H0vmjgcDmzevBmDBw/2TeBdkD/vRVZWFm6++WbPsdTUVMybNw9Wq9WHr4BCEftj6WB/LB3sj+lyRqOxWb0CuVwOnU6HsrKyoMQTFRWFxx9/HCqVCg0NDVi7di1mz54tyVgvp1arsW3bNrzxxhsApBnr4sWLMW/ePAwYMKDZfqnF6nA4YLPZ0LdvX5w6dQoDBw7EQw89JMlYS0tLIZfL8e9//xvHjx/HgAEDPFOQpBbr5Y4dO4aysjLccccdAKQZK5MaQVZSUgIhBH7yk5+gb9++zR5GoxEAMGLECCxZsgS/+MUvsG7dOjQ0NMDpdLb6fG63GzabzbO9Y8cOjBkzBmPGjMHevXtht9sD8rpCkb/vxVtvvYXY2FjExMTgwIEDeO211wLyukKRP+/FhQsXPHMCAUChUODtt99Gjx49/P/CSNLYH0sH+2PpYH9MVxJCtNi+vIBgMDidTsycORNyuRwrVqzw7JdirABw8OBBZGRk4De/+Y1nn5Ri/frrr3H48GEsW7as1eNSijU8PBwlJSV4/vnn0adPHyxevBjZ2dmor6/3xHa5YMZqtVrhcrmwfPlypKWlYdGiRdi9ezccDocntstJ5fP61VdfYcyYMYiNjfXsk1qsLVPoFFBHjhyBXq9vVqn5zJkzGDx4MBITEwE0/if/+OOP47bbbkNqaiqefvppbNiwAeHh4bDZbFAqlZg4cSKAxt+oREdHe6qM79y5Ew899BCGDBkCIQSysrJw2223Bfx1hgJ/34uZM2fimWeegdFoxBNPPIHf/va3+PTTTwP+OkOBP+9FQ0NDq7+xJWJ/LB3sj6WD/TFdLikpqdlKNi6XCyaTqVlyKtDcbjdmzJiBvLw8fPPNN1CpVACkF2tFRQVKSkowbNgwZGRk4KWXXkJGRgZqamokF+vnn3+O0tJS9OzZEwBQU1ODKVOm4N1335VcrDKZrFkBbp1OBwCora2VXKxarRYAEBcXBwDQ6/UQQqCqqkpysV7uq6++wt133+3ZlmSsAZzqQq148MEHxdSpU5vt27Nnj0hNTW31/Nzc3GbbTzzxhKdoy5XKysqETCYTUVFRQqvVCrlcLp566infBN4F+fNeXDmH+9ChQwKAqK6uvqaYuyp/3ov7779fLFq0yLPtcDhEWFiYOH369DVGTaGO/bF0sD+WDvbH3QO8rKmxZ88eERMT46mXcujQIaFWq4XVag1QpC1jWr58uRgyZEiLf8NSi3X79u0iKSnJc+zo0aMCgDCbzZKLtalwZNMjJiZGfPrpp6K2tlZysf7jH/8Q1113nefYzp07hVqtFm63W3Kx1tXVifDwcJGVlSWEEGLfvn0iLCxMNDQ0SC7WJlVVVSIsLMxT3FQIafzbuhKnnwRRYWEhPvvsM8+8rybl5eWtDr00Go0YPXo0Lly4AACwWCxYv349Fi1ahB07diA/P7/Z+d988w3i4uJw8uRJHDt2DE888QR27tzpvxcUwvx9L64kfhiy1dbw3O7M3/di2LBhOHTokGc7Ly8Pbrfb89sI6p7YH0sH+2PpYH9MVxo3bhwSEhKwfPlylJSUYMWKFbjvvvugVquDEo/RaMSaNWvw1ltvAWhc2tNkMsHtdksu1pEjR8Jms+HNN99ESUkJVq9ejfHjxyMmJkZyscbHxyMtLc3zkMvlMBgMiI6Ollyso0aNQnl5Od577z3k5+cjMzMTc+bMgUwmk1ysKpUKU6dOxbPPPovz58/jtddew+TJk6FQKCQXa5Ovv/4aiYmJGDJkiGefFGNlUiNIbDYbZs2ahdGjR+Oee+5pdqy0tNQzpPNyS5Yswdy5cz1DrDIzM7FgwQIMHz4c27Ztw8MPP9zs/B07duC2227zdEjTp0/H0aNHUVFR4b8XFoICcS8AwG63w2Qy4fTp03jmmWcwYMCAZkV2KDD3Ys6cOcjJycGqVauQn5+P3/3ud7jjjjug0Wj898JI0tgfSwf7Y+lgf0ytkcvl2LhxI7Zs2YK+ffvCZrNh9erVQYtn+/btMJvNGDt2LHQ6nedRVFQkuVhjY2Pxt7/9De+++y4GDBiAsrIyfPzxxwCk9762R2qxJiQk4PPPP8eqVauQkZGB5ORkvPLKK5KMFQDWrVsHIQQGDRqEsrIyT7FYKcYKNE49mTx5crN9kow1aGNEurFjx46JQYMGifT0dFFaWurZv3//fvHhhx+K/v37i9/+9rfN2vz1r38VSUlJora2VgjRuLb1mDFjhNPpFA6HQ5w4cUJotVrx3nvvedokJyeLd955x7PtdDqFRqMRGzZs8PMrDB2Buhdz5swRAAQAodFoxOTJkz1rqlOjQN0LIYTYu3evGDRokIiKihJTp04VRqPR/y+QJIn9sXSwP5YO9sdERBRKmNQIgp07d4p77rlHVFZWNtu/evVq0aNHDzF9+nRRVFTk2V9aWtriB4GUlBSRkpIibrrpJjFhwgQxd+5cMXfuXKHX61s8L7WN90I6eC8oGPi5kw7eC+ngvSAiolAiE+KK9VhIkrKzszFixAhPlfDs7Gzo9Xr069ev2Xkff/yxZ0kr8g/eC+ngvaBg4OdOOngvpIP3goiIgoVJDSIiIiIiIiIKSUyTExEREREREVFIYlKDiIiIiIiIiEISkxpERERERETdzOeff47MzMyAXpOVD8gfWFODiIiIiIioC3O5XJDJZM2K9L755pt45plnUFlZ2excp9MJhULh2U5NTYXVam1R4Leurg6PPfYYnn/++Wb7P/30U6hUKtTU1GDr1q14+umn8fbbb2PdunVYuXIlvv/+e3zwwQetxjlixAg4nU5otdoWxxwOB06ePAmz2dzZl09dnKLjU4i6t/z8fCiVSiQlJQU7FCKibot9MRHR1fv1r3+NDz/8EBEREZ5VioQQcLlciI+P95xns9kwatQo/POf//TsUyqV2LdvH9LS0po957PPPtvqSkbjxo3DX/7yFyQmJiI8PByHDx/GvffeC7lcjv/85z8YN24cCgoKAAButxvXXXedp21YWBicTmebr6O1ZAcRkxpEHZg+fTruuusurFy5stXjubm5+Oijj/DMM88gPDy82TGNRoN///vfGDBgQCBCJSLqstgXExFdvXXr1mHdunUAgMOHD+PixYu45557AAB5eXnYuXMnfvnLX0KlUrVo294SzFceu3DhAgYNGoSYmBjU1NRACIGsrCxYrVZs3LgRe/fuhUwmw549e7Bt2zbMmDEDH374YbPne+GFFzBq1KgW1yovL8dtt912Va+fujYmNYg6MGvWLJSXl7d5PDIyEl9//TW++eYb/PWvf0XPnj09xyIiIqBUKgMRJhFRl8a+mIjINw4cOID//d//9SQ1/v73v2P16tX46U9/2mpSw+12t/lcVx5LTk6GyWTCc889h3fffRcTJ05EWloa5s6di8OHD2PYsGH4y1/+gsrKSmRkZDRLaADAjBkzsHv3buzevbvV6/3qV7/q7MulboBJDaIfnDp1CuPHj4darfYMy7NarVCpVJDL5di4caPnXLfbjbq6OuTk5KBXr17Yv38/Xn/9deh0OsyaNQv/+te/IJfLYTKZcOutt0KhUMBms2HDhg245ZZbgvUSiYgkj30xEZF//fznP0d9fT3cbjfkcjk+++wzLFu2DHq9HkBj/Y2m/hcA1Go1fvSjH7X6XI899liz7crKSixcuBA5OTl48sknkZWVBZ1OhwULFsBqtUKtVgMATCYTEhISPO2+/PJLvPTSS9BqtYiJiWkzdrvdjnHjxuGxxx7Dvffee9XvAXUtLBRK1A61Wo1du3Zh9OjRbZ5TX1/fLKtts9mgUCigUChgMBiQnZ2NtLQ02Gw2hIeHN/tPgoiIOsa+mIjo2o0fPx7fffddiyl6lycxXC4XLBYLSktLm9Xa8Jbb7cbvfvc7/P73v8fOnTsxZswY6HQ67N27F0899RR0Oh127tyJ7OxsrFy5Elu2bGnWfteuXaiqqmrz+cPCwpjMoBa4pCtRG1wuF2w2W7vZ4gMHDiA9PR2bNm3y7FMqlc0qRl++nz9EExF1DvtiIiLfsNls+NOf/oRLly41e1RXVzf7e0NDA+Lj4zFy5EjEx8c3e+Tn5+Pll19usf+5554D0NhnZ2ZmwmKxYM6cOVCpVFCr1bjrrrtw9OhRDB48GLt27cKpU6fQr1+/FjH+4Q9/QEVFBQwGAwwGAxYsWACVSgWDwYCIiAgsWLAg0G8bhQBOPyFqQ0lJCYQQ+MlPftIio71//34YDAaMGDECS5YswS9+8QusXr0aDz/8MGQyWas/SLvdbjgcDs7rJiLqBPbFRES+UVpa2qnVQywWC/bv34/+/fsDANLS0hAWFgaXy4VFixbh2WefBQC8/PLLsFqtAIDf//732LBhA8xmMyIjI5GRkYGKigq8/vrr+NWvfoUf//jH2LBhA8LCwjBx4sQW11QoFHj33Xc9cdbU1OCll15CeHg4HA4Hk9LUKiY1iNpw5MgR6PV6FBYWevadOXMGgwcPRmJiIoDGjvfxxx/HbbfdhtTUVDz99NPYsGEDwsPDYbPZoFQqPR22y+VCdHQ0Tp8+HYyXQ0QUktgXExFdO7PZjNLSUgwcONDrNjKZrMU+uVze6mooTcmGV155BZmZmRgyZAg2btyI66+/Hv369cPdd98NALjjjjvw8MMPIzw8HC+//HKL53E4HBg9ejR69+4NADh69Chuv/12qNVqWCwWrF271uv4qftgUoOoDV9++SVuvvnmZvvKy8thMBhadPIZGRk4d+4cVq1ahVWrVgEAnnzySVRUVODPf/5zwGImIupq2BcTEV27zZs3Iz09HcnJyV63aS2p4Xa7W91/uYKCAvTr1w8PPPAA4uLiEBsb60mEqFQq3HTTTbDZbOjRo0ezdi6XC59++mmz53/ttddw7733QqfTAQBmz57dopApEZMaRK0oLCzEZ599hi+++KLZ/vLy8hYdMAAYjUaMHj0ax48fR3JyMiwWC9avX48vv/wSO3bsQL9+/dCnT59AhU9E1CWwLyYiunYmkwnPPPMMFi9e3Kl2brcbY8aM8SQQqqur4XQ64XK58Morr+CNN94A0Fio+fJVUNLT07F+/XosX74c//znP2EwGHDnnXfi22+/xeLFi1FbWwuj0Yg//OEPeOGFFzztTpw4gXnz5jUr+lxVVYVFixYhPDwcQgjYbDa8/vrrGDly5LW8JdTFsFAo0RVsNhtmzZqF0aNHe9bvblJaWuoZ7ny5JUuWYO7cuZ7sd2ZmJhYsWIDhw4dj27ZtePjhhwMSOxFRV8G+mIjIN/Ly8nDDDTdg4cKFnWrX0NCAAwcOeIqIpqamwul0wm6348knn/TsX7ZsGex2OwAgJycHixYtwoABA+B2u7Fv3z589dVXmDlzJtLT01FXV4fdu3dj8+bNePvtt3HffffB7XZj69atWLJkCQwGA2JjY1t96HQ6JCUl4emnn8Z7773nj7eKQhSXdCW6zPHjxzF79mzU1dV5CtABQFZWFs6fP4/MzEzcddddWLNmjafN3/72NyxatAhnz55FdHQ0vvrqK7zwwgvYt28f3G43vv/+e4wbNw6vvvoq5s6dG6yXRkQUMtgXExEFn9lsRnR0dIsaGjabDQBaLbhcUFCAzz77DA8++KCn7waA7OxsXLp0CVOmTPHsKywshMViwY033uinV0DdBZMaRJf55ptvsHbtWnzwwQeIi4vz7F+zZg1efvlljB07FmvXrkVqaiqAxqHO/fv3b/ZDctOx2NhY6PV69O3bFwDw97//HWfPnm32vERE1BL7YiIiIvIWkxpE1yg7OxsjRozwzDfMzs6GXq9vsfb2xx9/jJkzZ7ZaMZqIiK4N+2IiIqLuiUkNIiIiIiIiIgpJ/DUFEREREREREYUkJjWIiIiIiIiIKCQxqUFEREREREREIYlJDSIiIiIiIiIKSUxqEBEREREREVFIYlKDiIiIiIiIiEISkxpEREREREREFJKY1CAiIiIiIiKikPT/AcgJ6oHJfDeAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1300x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 选择适当的图形和统计量比较3种方法组装产品数量的分布特点。\n",
    "# 这里选择箱型图、点图和核密度图\n",
    "plt.figure(figsize=(13, 4)) # 设置画布大小\n",
    "\n",
    "plt.subplot(131) # 创建子图1\n",
    "sns.boxplot(x='方法', y='产品数量', data=df_melt) # 绘制箱型图\n",
    "plt.title('(a) 箱型图') # 设置标题\n",
    "\n",
    "plt.subplot(132) # 创建子图2\n",
    "sns.stripplot(x='方法', y='产品数量', data=df_melt, jitter=True, size=5) # 绘制点图\n",
    "plt.title('(b) 点图') # 设置标题\n",
    "\n",
    "plt.subplot(133) # 创建子图3\n",
    "sns.kdeplot(x='产品数量', hue='方法', data=df_melt, fill=True) # 绘制核密度图\n",
    "plt.title('(c) 核密度图') # 设置标题\n",
    "\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>方法</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>方法A</th>\n",
       "      <td>165.6000</td>\n",
       "      <td>2.1314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>方法B</th>\n",
       "      <td>128.7333</td>\n",
       "      <td>1.7512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>方法C</th>\n",
       "      <td>125.5333</td>\n",
       "      <td>2.7740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean     std\n",
       "方法                   \n",
       "方法A  165.6000  2.1314\n",
       "方法B  128.7333  1.7512\n",
       "方法C  125.5333  2.7740"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 除了图形也可以从描述统计量上比较\n",
    "# 这里选择均值和标准差\n",
    "df_melt.groupby('方法')['产品数量'].agg([np.mean, np.std]).round(4) # 计算描述统计量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的结果可以看出：\n",
    "- 方法A的平均值明显高于其他两种方法，说明平均产出量最高；\n",
    "- 并且方法A的标准差略高于方法B，而低于方法C，方法A的稳定性也能接受；\n",
    "- 综上所述，方法A是最优的。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>男生</th>\n",
       "      <th>女生</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>182.1</td>\n",
       "      <td>175.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>183.3</td>\n",
       "      <td>162.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176.6</td>\n",
       "      <td>174.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>177.3</td>\n",
       "      <td>164.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.3</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      男生     女生\n",
       "0  182.1  175.4\n",
       "1  183.3  162.7\n",
       "2  176.6  174.6\n",
       "3  177.3  164.8\n",
       "4  174.3  157.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap03/exercise3_3.csv', encoding='gbk') # 读取数据\n",
    "df.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>身高</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>男生</td>\n",
       "      <td>182.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男生</td>\n",
       "      <td>183.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>男生</td>\n",
       "      <td>176.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>男生</td>\n",
       "      <td>177.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>男生</td>\n",
       "      <td>174.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   性别     身高\n",
       "0  男生  182.1\n",
       "1  男生  183.3\n",
       "2  男生  176.6\n",
       "3  男生  177.3\n",
       "4  男生  174.3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_melt = pd.melt(\n",
    "    df, value_vars=['男生', '女生'],\n",
    "    var_name='性别', value_name='身高'\n",
    ") # 数据重构\n",
    "df_melt.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 利用图形和描述统计量比较分析男女学生身高的特点和差异。\n",
    "# 这里选择小提琴图和直方图\n",
    "plt.figure(figsize=(9, 4)) # 设置画布大小\n",
    "\n",
    "plt.subplot(121) # 创建子图1\n",
    "sns.violinplot(x='性别', y='身高', data=df_melt) # 绘制小提琴图\n",
    "plt.title('(a) 小提琴图') # 设置标题\n",
    "\n",
    "plt.subplot(122) # 创建子图2\n",
    "sns.histplot(x='身高', hue='性别', data=df_melt, bins=10, kde=True) # 绘制直方图\n",
    "plt.title('(b) 直方图') # 设置标题\n",
    "\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>ptp</th>\n",
       "      <th>cv</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>女生</th>\n",
       "      <td>167.856</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.0294</td>\n",
       "      <td>0.1802</td>\n",
       "      <td>0.0244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>男生</th>\n",
       "      <td>178.560</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.3658</td>\n",
       "      <td>-0.1379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean   ptp      cv    skew  kurtosis\n",
       "性别                                         \n",
       "女生  167.856  22.2  0.0294  0.1802    0.0244\n",
       "男生  178.560  29.5  0.0373  0.3658   -0.1379"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 除了图形也可以从描述统计量上比较\n",
    "# 这里选择平均值、极差、变异系数、偏度系数和峰度系数\n",
    "# 变异系数的函数\n",
    "def cv(x):\n",
    "    return x.std() / x.mean()\n",
    "df_melt.groupby('性别')['身高'].agg([np.mean, np.ptp, cv, stats.skew, stats.kurtosis]).round(4) # 计算描述统计量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的结果可以看出：\n",
    "- 男生的身高平均值高于女生，但是极差也更大，说明男生的身高差异性较大大；\n",
    "- 男生身高的变异系数也大于女生，同样反映出男生身高的相对离散程度更大；\n",
    "- 女生和男生的身高都呈现出右偏分布，但是女生的身高峰度系数更大，呈现尖峰分布。"
   ]
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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